既有建筑改造推荐系统

我国建筑中95%以上都是高能耗建筑，建筑产生的能耗占到了全社会总能耗的40%，因此，建筑节能是实现我国降低碳排放的重要内容。本研究通过对武夷山市星村镇的旧宅进行实地测量，建立revit模型，并导入Green Building Studio进行能耗及热舒适度模拟分析，最后得到改造方案。研究发现应用BIM工具对既有建筑的改造进行分析，可以得到有效合理的改造建议，能让改造方案少走弯路。

本文梳理了国内外建筑装修工程行业发展的现状，对在双碳目标下国家对建筑装修工程的要求及影响进行了分析，在此基础上，分别从建筑装修工程项目全生命周期的不同阶段，提出在双碳背景下建筑装修工程管理的应对策略。

作为城市更新行动至关重要的一步，老旧小区改造持续受到社会的关注。为了进一步探索我国老旧小区改造的可持续机制，借助CiteSpace知识图谱分析软件，对中国知网数据库中2013~2021年中关于中国老旧小区改造研究的一百七十二篇学术论文进行了可视化分析，并绘制出关键词时区图谱、高频关键词共现图谱和聚类图谱，来分析老旧小区改造的热点问题和趋势。通过分析老旧小区改造过程所存在的现状与问题，提出了相应的解决路径。研究结果可以为合理地制定老旧小区改造策略提供参考。

在全球气候变暖与能源短缺日益严峻的背景下，如何在建筑行业乃至人们的日常起居中做到绿色低碳不仅是国家层面的战略问题，更是与全人类未来发展息息相关的现实挑战。人工智能是赋能建筑行业中的新兴技术，可作为一种运用数字模型运用于建筑中碳排放量的动态预测分析与节能路径优化，让建筑中各要素碳排放权重更加合理，施工工艺更加节能。研究表明，我国建筑行业碳排放的实时管控面临着排放因素复杂多变，碳排放监测额外支出，以及排放量不直观、难以实时观测等诸多挑战。而人工智能在构建排放观测的实时传感系统的基础上，能通过智能能源管理系统、区块链等技术，利用现有数据拟合碳排放数据情景，提供可靠、精确的排放数据，将排放逻辑与机器学习深度融合，创立更加公开透明的碳排放额度公示制度，助力我国“双碳”政策在建筑行业落实，而这也将推动碳管理技术朝着数字化、智能化、低碳化和全生命周期管理的方向快速发展。

我国建筑能耗在社会总能耗中占比较大的比例，因此建筑节能不容小觑。本文分析我国目前的能耗现状，介绍建筑节能的设计原则，并对建筑能耗进行分析，提出一定的节能措施。

近年来，我国大力提倡绿色建筑，推动城市建筑节能减排，并且已逐渐提高至国家政策层次。绿色建筑是一门新兴的以环境为优先、绿色发展理论为基础的现代建筑科学。其实质是与自然界的可持续发展、和谐共存，与人类生态环境体系中的共同和谐。在城市更新高速增长的时代，我们已经将目光聚焦到了未来的都市建设上，但对老旧小区中大量的既有建筑的改造却还不能取得充分的重视，并且忽略了老旧小区中现有房屋改造对城市建设质量提升和节约减碳的重要意义。文章将通过文献研究，总结出了相应的老旧小区建筑改造绿色技术应用现状，根据老旧小区绿色改造的问题，提出了从评估到技术创新、资金模式建立再到居民教育和后期管理的绿色建筑技术在老旧小区建筑改造中的应用策略，力求为当前城市中老旧小区的建筑绿色化改造提供参考和依据。

在“碳达峰、碳中和”的战略目标以及绿色发展的时代背景下，城市老旧小区的绿色低碳化改造对促进城市面貌更新、提升城市人文形象和保护人民居住环境至关重要，是实现既有建筑可持续发展的必要手段。因此，本文以扎根理论为依据展开质性研究，针对锦州市老旧小区居民进行大规模的访谈，对研究所需的研究资料进行收集，并在此基础上制定理论研究模型，深入分析老旧小区改造绿色发展的路径。研究结果显示，老旧小区改造的绿色发展既受到技术水平、生态环境、社会影响、经济价值、绿色创新能力和绿色智力资本这些自身能力的影响，也受到市场压力和环境动态性等外部环境的影响。本研究为各老旧小区的低碳化改造提供经验和借鉴，对探索老旧小区改造的绿色发展路径具有一定的意义。

随着城市化推进，老旧小区普遍存在基础设施老化、公共服务资源短缺、生态环境退化等诸多困境，这些状况严重影响了城市的可持续发展水平和居民的生活品质，在绿色发展理念同信息技术紧密结合的当下，促使老旧小区朝着新型绿色智慧社区方向转型有着十分重大的战略意义，由于没有统一的改造标准，所以改造效果存在明显的差异性，本研究主要针对老旧小区智慧社区创建的核心价值以及在主体界定、目标规划、资源调配等方面所遇到的难题展开探讨，形成包含基础设施更新、环境改善、安全保障加强在内的智能化改造技术规范体系，通过典型案例剖析其实际应用前景，从而给老旧小区绿色智慧转型给予理论依据和操作指引，助力居民福利提升和城市高质量发展。

近年来，随着我国推进城市更新的进程不断提速，老旧小区的改造工程持续增长。为了保障投资效益与项目质量，项目初期对工程造价进行科学、准确的评估已成为关键一环。针对传统预测方法在参数调优方面存在经验性强、效率低等问题，本文提出结合贝叶斯优化算法与支持向量回归(SVR)模型的方法。该模型可自动优化SVR的关键超参数，从而提升模型对复杂非线性数据的适应性与预测准确性。研究选取华东某省会城市200个老旧小区改造项目作为样本，提取了九项影响因子，并利用均方根误差(RMSE)、平均绝对误差(MAE)和决定系数(R²)等指标对比分析了标准SVR与BO-SVR模型的性能。结果表明，BO-SVR模型在预测精度和稳定性方面均优于传统模型。该方法为城市更新工程提供了有效的成本估算工具，具有良好的实际应用前景。

随着全球气候变暖，二氧化碳排放量的不断增加，污染愈演愈烈，低碳环保已成为当前建筑设计的首要考虑因素。建筑物成为了二氧化碳的主要释放中心，急需在设计建造时考虑整个使用过程中的低碳性能。为了研究低碳建筑性能设计，本文首先简述了当代低碳建筑的节能理念和现实意义，然后系统分析在低碳模式下建筑物的主要碳排放活动，并运用大数据分析和多因素数据分析，形成了一个低碳建筑性能设计的评估系统，并优化了工程设计中的技术。实验结果可为低碳建筑性能设计提出指导建议，为低碳建筑设计与管理提出理论依据等。

我国作为一个发展迅速的发展中国家，建筑业发展迅速，建筑数量不断增加，城市化进程也在不断推进，但同时也面临着建筑能耗总量持续增长的挑战。本文结合重庆地区的地域气候特征，选取重庆市某行政区内拟建的一栋移动通信枢纽大厦作为研究对象，利用Autodesk Revit软件和斯维尔节能软件，对办公建筑进行节能分析。通过对采光方面的模拟分析，发现建筑不满足节能标准的问题，提出相应的优化方案，并进行优化前后的能耗对比分析。旨在为绿色办公建筑节能减耗提供分析方法和思路，为绿色办公建筑优化设计提供参考。

在碳达峰、碳中和的时代背景下，酒店建筑能耗较高，是节能减碳的重要一环，对碳中和具有重要意义。酒店建筑的表皮设计，可以通过自遮阳等方式节约大量能源，从而降低碳排放。基于以上思考，文章构建了基于光照和热稳定性的建筑表皮优化流程，旨在能够对建筑遮阳及采光之间的矛盾与冲突进行合理的取舍，为相关建筑设计提供一定的启发。本研究以某酒店建筑的一间南向客房为例，在Rhino平台构建参数化模型，利用Honeybee和Ladybug两款插件，对建筑进行采光模拟和辐射得热模拟，同时采用Octopus进行多目标遗传算法优化，得到了建筑表皮方案的24个相对优解，并综合考虑采光与遮阳效果，得到了建筑表皮的最终方案。

随着全球资源紧张与城市扩张的加速，逐渐存量建筑的更新和室内资源的再利用成为实现城市更新中可持续发展的关键路径。建筑拆除和室内装修产生的大量废弃物，给环境带来了沉重负担，而如何在更新改造中有效利用现有资源，已成为社会各界关注的焦点。本文探讨了如何通过再利用建筑与室内材料减少环境负荷、延长资源的生命周期，并提升空间的适应性与价值。结合具体案例，分析存量建筑更新中的资源管理策略与室内设计实践，展示如何通过创新室内设计的手段实现“旧物新生”，不断推动绿色发展和循环经济。

在公共建筑绿色改造项目中，政府委托ESCO进行能源管理，但ESCO可能牺牲节能效率谋利。本文基于委托代理理论，分析政府与ESCO的行为策略，提出以下措施减少ESCO机会主义行为：1) 提高能源服务费或加强监管以激励ESCO绿色改造；2) 提升服务费以增强设施维护；3) 设置高额奖惩机制保障节能设备质量，但可能增加业主费用；4) 合理设定服务费和奖惩系数以促进高效节能。研究有助于政府激励ESCO提高设备寿命和能源效率。

在当今全球气候变化和能源危机日益严峻的背景下，建筑行业作为能耗大户，急需转型升级以实现可持续发展。面对这一挑战，本文聚焦于绿色建筑的美学价值与实践路径，旨在探索如何在确保环境可持续性的同时，提升建筑的美学体验。本文揭示了融合建筑美学与可持续能源政策的可能性和有效性，为绿色建筑的发展提供了新的视角和实践指导。通过精心设计和技术创新，绿色建筑不仅能够达到节能减排的目标，还能提供独特的美学体验和文化价值。这一发现对于推动建筑行业的可持续发展具有重要意义，它不仅有助于提升公众对绿色建筑的认知和接受度，还为建筑师和决策者提供了实现环境目标与美学追求双重目标的有效路径。

在全球低碳发展战略背景下，绿色建筑成为建筑行业转型的核心方向，而节能保温材料的合理应用是实现绿色建筑低碳目标的关键路径。本文以低碳目标为导向，聚焦绿色建筑设计环节，系统分析节能保温材料的类型与特性，通过实际工程案例探究不同节能保温材料在保温隔热、低碳减排等方面的应用效果，并从初期投资、运营成本、全生命周期成本等维度对其经济性进行深入分析。研究结果表明，优质节能保温材料虽在初期投资阶段略有增加，但能显著降低建筑运营能耗与碳排放量，且从全生命周期视角具备良好的经济性。本文的研究可为绿色建筑设计中节能保温材料的选型与应用提供理论参考和实践依据。

Existing buildings have a significant impact on the environment, especially during the operation and maintenance (O&M) phase, in which they continuously release carbon and consume resources. Retrofitting buildings is an important approach to increase their energy efficiency and reduce their environmental impact. This paper develops a digital platform using building information modeling (BIM) models to collect and analyze necessary data for planning and executing building retrofit projects in accordance with the LEED O+M rating system. The platform consists of four modules. The data preparation module prepares and compiles data from BIM models and other data sources. The LEED credit evaluation and selection module analyzes LEED credits to support decision-making. The project planning and control module tracks the project’s credit status and documents. The reporting and visualization module presents important project data in visual and interactive formats. This platform can assist all project participants in the planning and execution processes, allowing them to share project information via a common database. They can clearly understand their roles and the activities for which they must be responsible at each stage. This platform can also manage project data systematically and efficiently, increasing the opportunity to achieve sustainability goals per the LEED O+M rating system.

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China’s urbanisation has transitioned from an era of rapid, coarse expansion to one of refined and targeted development. In accordance with China’s “dual-carbon” strategy, the building sector—presently the third-largest source of domestic carbon emissions—is compelled to pursue emission optimisation in its forthcoming evolution. Photovoltaic-building technologies offer an effective response to this imperative. Within the context of accelerating high-rise residential construction, the architectural integration of scientifically configured photovoltaic façades has emerged as a critical challenge. Employing an integrated methodology of urban surveying and simulation, this study examines the façade characteristics of residential buildings in northern Chinese cities, selecting Xi’an as the representative case. Three PV-facade integration strategies for existing stock are presented: window retrofitting, wall retrofitting, and full-façade renovation. Utilising the EnergyPlus platform, the manuscript simulates the electrical demand profiles and clean-electricity generation of typical dwellings under varying photovoltaic materials and configuration schemes, while concurrently assessing economic performance. It demonstrates that a judicious determination of photovoltaic installation scale and layout strategy markedly amplifies energy-saving efficacy, diminishes aggregate energy consumption and carbon emissions, and simultaneously reduces the capital expenditure of photovoltaic systems. For multi-story buildings, a full façade retrofit yielded the highest annual electricity generation of 514,703.56 kWh and an annual carbon reduction of 15,521.50 kgCO2. For high-rise buildings, installing PV modules only above the 20th floor increased the effective generation ratio from 45.24% to 87.17%, while the carbon reduction efficiency per unit investment improved from 0.05 to 0.22 kgCO2/¥.

This paper highlights the role of building retrofitting in developing energy-resilient communities as a part of sustainable urban regeneration. Different approaches and technologies are covered, with the role of improving the energy performance of existing buildings by utilizing, among others, innovative insulation materials or renewable sources for heat supply combined with advanced smart control systems. The case studies from different parts of the world illustrate that this techno-economically viable retrofitting approach can reduce around 40 % energy consumption and emissions, making buildings more sustainable. The analysis of the new economic and regulatory is connected with the government’s incentives as well as public engagement in developing positive energy communities. This paper also documents an extensive evaluation of retrofit technologies and their application, demonstrating the critical contribution energy retrofits can make towards achieving enduring urban sustainability.

Retrofitting community buildings is a key pathway toward carbon neutrality, yet most existing retrofit planning models lack adaptability to the diverse urban contexts of the Global South, where building typologies are heterogeneous and resources limited. Addressing this gap requires approaches that are both computationally efficient and context-sensitive. This study introduces a hybrid optimization framework that integrates Genetic Algorithm (GA) and Mixed-Integer Linear Programming (MILP) to tackle the multidimensional multiple-choice knapsack problem inherent in retrofit planning. The GA explores high-level system configurations, while MILP ensures precise component-level selection under budget and technical constraints. Compared to conventional single-method approaches, the hybrid GA–MILP achieves near-optimal emission reduction with reduced computation time and greater feasibility, offering a balanced trade-off between performance and scalability. Importantly, the framework demonstrates that medium-cost retrofit strategies provide the most cost-effective path to scalable carbon savings, making it highly relevant for resource-constrained urban environments. By situating retrofit planning within the realities of the Global South, this study advances methodological innovation and provides a robust decision-support tool aligned with sustainable development goals for inclusive and low-carbon urban futures.

The current energy-environmental emergency requires urgent actions, especially for retrofit existing buildings in line with EU Renovation Wave targets and decarbonization strategies. The paper presents the optimization process of a dry envelope system based on sandwich panels, developed and engineered within “RE-SKIN” Horizon project. Rather than introducing new materials, the approach upgrades state-of-the-art technologies to enhance performance while minimizing environmental impacts. Through close collaboration with consortium partners, the design evolves from conventional to low-impact materials, integrating low-impact coat steel layers and bio-based PUR foam insulation. A comprehensive Life Cycle Assessment (LCA) identifies environmental hotspots, guiding impact reductions between the initial and optimized design solution. The suggested sandwich panels enhance energy efficiency while enabling smart building integration for real-time monitoring, all while supporting circular resource use as a sustainable alternative to standard insulation coatings.

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Achieving climate neutrality requires a fundamental transformation of production systems and energy use, driven by technological innovation. In the building sector, virtual representations of physical assets can accelerate this transition by enabling simulation-based evaluation of energy strategies. When combined with Reinforcement Learning (RL), these models support dynamic testing and real-time optimization of building operations. This study presents a simulation framework for assessing and comparing energy management strategies aimed at reducing energy consumption while maintaining thermal comfort. As a case study, the methodology is applied to an existing industrial facility using the BIMtoBEM modeling approach. The framework integrates detailed simulation models with RL-based control to optimize the performance of the Heating, Ventilation, and Air Conditioning (HVAC) system. Two digital models with increasing Levels of Detail, are developed to evaluate the impact of three structural and one mechanical refurbishment scenario, alongside two RL control strategies. By simulating different combinations of physical retrofits and control approaches, the framework enables users to identify the most impactful interventions and make informed decisions based on specific energy-saving goals. Results show that modifying the mechanics of the HVAC system alone leads to a 12 % reduction in natural gas consumption, while combining retrofitting with RL can lead to 32 % of savings, emphasizing the impact of both physical and control-based interventions.

The energy efficiency and sustainability of existing buildings have become a critical concern in Algeria’s efforts to reduce energy consumption and mitigate environmental and economic impacts. To address this challenge, a systematic and effective decision-making method is required to select optimal building retrofit measures in alignment with Algeria’s 2030 energy strategy. In this study, we propose an innovative approach based on the Fuzzy Analytical Hierarchy Process (FAHP), a widely used multi-criteria decision-making method, to evaluate and prioritize different retrofit measures. The FAHP allows decision-makers to have a comprehensive framework for making informed choices by incorporating independently proposed economic, environmental and technical criteria. The results demonstrate the high significance of retrofit measures that enhance thermal insulation, with double glazing and roof insulation emerging as top priorities. Sensitivity analyses confirm the stability and robustness of the decision-making process. This approach offers valuable insights for policymakers and building professionals seeking to implement sustainable and energy-efficient retrofitting strategies in Algeria’s construction sector. By aligning with the country’s energy goals, this decision-making method contributes to achieving a more sustainable and environmentally responsible built environment.

To achieve climate and energy goals in the building sector, the current pace of renovating existing structures must double, overcoming prevailing barriers. Key Performance Indicators play a pivotal role in science-based decision making, emphasizing both direct and indirect benefits of building retrofits. The authors aim to contribute to proper metric identification for multi-perspective building performance assessment and formulate a methodology supporting energy planning decisions. They introduce the Global Cost per Emission Savings (GCES), an aggregated indicator encompassing both public (CO2 emissions) and private (global cost) perspectives of diverse retrofit technologies for building HVAC systems. Applied to the Italian residential building stock via the Reference Building approach, the methodology is tested using condensing gas boilers, biomass boilers, and electric heat pumps, revealing diverse environmental and economic performances. Addressing the establishment of effective decision-support tools for policymakers, the paper explores the potential impact of various policies on the favorability of technologies. Different policy scenarios are delineated to analyze how distinct approaches may influence the attractiveness of technologies. Notably, in the baseline scenario, biomass boilers hold an advantage over heat pumps according to the GCES index. However, scenarios involving technology-specific incentives or a greenhouse gases emission tax failed to alter the technological ranking, leaving heat pumps financially uncompetitive. In contrast, the TXPM scenario positions heat pumps as the most financially appealing option, penalizing biomass boilers for high particulate matter emissions.

In the context of global warming, building transformation takes on a dual responsibility to be more energy-efficient and sustainable for climate change mitigation and to be more climate-resilient for occupants’ comfort. The building energy retrofitting is an urgent need due to the large amount of existing building stock. Especially in high-rise and high-density cities under a subtropical climate, like Hong Kong, existing buildings with large glazed façades face the challenges of high energy consumption and overheating risks. An advanced glazing system, namely the vacuum insulating glazing (VIG), shows the potential for effective building envelope retrofitting due to its excellent thermal insulation ability. Yet, its performance for practical applications in the subtropical region has not been investigated. To enhance the energy performance and thermal comfort of existing high-rise buildings, this study proposed a novel retrofitting approach by integrating the VIG into the existing window system as secondary glazing. Field experiments were conducted in a commercial building in Hong Kong to investigate the thermal performance of the VIG retrofit application under real-world conditions. Furthermore, the energy-saving potential and thermal comfort performance of the VIG retrofit were evaluated by building energy simulations. The experimental results indicate that the VIG retrofit can effectively stabilize the fluctuation of the inside glass surface temperature and significantly reduce the heat gain by up to 85.3%. The simulation work shows the significant energy-saving potential of the VIG retrofit in Hong Kong. For the VIG retrofit cases under different scenarios, the energy-saving potential varies from 12.5% to 29.7%. In terms of occupants’ thermal comfort, the VIG retrofit can significantly reduce the overheating risk and improve thermal satisfaction by 9.2%. Due to the thermal comfort improvement, the cooling setpoint could be reset to 1 °C higher without compromising the overall thermal comfort. The average payback period for the VIG application is 5.8 years and 8.6 years for the clear glass retrofit and the coated glass retrofit, respectively. Therefore, the VIG retrofit approach provides a promising solution for building envelope retrofits under subtropical climate conditions. It not only benefits building owners and occupants but also contributes to achieving long-term climate resilience and the carbon neutrality of urban areas.

No abstract available

This study examines existing buildings in Haikou in China under tropical island climate conditions. It presents three retrofit design models for greenhouses roofs (GHR), green roofs (GR) and photovoltaic roofs (PVR). The carbon cost of each retrofit roof model is calculated in the production and transportation phases of building materials, construction, and demolition. The changes in electricity consumption, water consumption, and plant carbon reduction are coupled to calculate the carbon reduction generated by each phase of the use of the retrofitted roofs. The carbon reduction per unit area for GHR, GR and PVR over the life cycle (20 years) is then comprehensively calculated. The life cycle carbon reduction per unit area is 262.57 kg·m−2 for GHR, 127.41 kg·m−2 for GR and 2567.12 kg·m−2 for PVR. Among the three retrofit methods, PVR has the greatest potential for reducing carbon emissions. The study can as a guide for implementing carbon reduction measures in tropical island areas. Domestic research on rooftop greenhouses also focuses on technology, yield, and energy consumption, mostly for northern regions with cold winters, and less research on rooftop greenhouses applied to regions with hot summers and warm winters. But domestic and foreign studies on the potential of rooftop greenhouses to reduce emissions have not yet been combined with plant cultivation of hydroelectric carbon emissions and plant carbon sequestration.

Improving energy efficiency in residential buildings is critical to combating climate change and reducing greenhouse gas emissions. Retrofitting existing buildings, which contribute a significant share of energy use, is therefore a key priority, especially in regions with outdated building stock. Artificial Intelligence (AI) and Machine Learning (ML) can automate retrofit decision-making and find retrofit strategies. However, their use faces challenges of data availability, model transparency, and compliance with national and EU AI regulations including the AI act, ethics guidelines and the ALTAI. This paper presents a trustworthy-by-design ML-based decision support framework that recommends energy efficiency strategies for residential buildings using minimal user-accessible inputs. The framework merges Conditional Tabular Generative Adversarial Networks (CTGAN) to augment limited and imbalanced data with a neural network-based multi-label classifier that predicts potential combinations of retrofit actions. To support explanation and trustworthiness, an Explainable AI (XAI) layer using SHapley Additive exPlanations (SHAP) clarifies the rationale behind recommendations and guides feature engineering. Two case studies validate performance and generalization: the first leveraging a well-established, large EPC dataset for England and Wales; the second using a small, imbalanced post-retrofit dataset from Latvia (RETROFIT-LAT). Results show that the framework can handle diverse data conditions and improve performance up to 53% compared to the baseline. Overall, the proposed framework provides a feasible, interpretable, and trustworthy AI system for building retrofit decision support through assured performance, usability, and transparency to aid stakeholders in prioritizing effective energy investments and support regulation-compliant, data-driven innovation in sustainable energy transition.

Indonesia has set a roadmap to achieve energy-efficient buildings by 2050. This study explored potential energy demand reduction through building retrofit between 2024 and 2050, aligning with Indonesia's roadmap and Paris Agreement to promote Nearly-Zero Energy Buildings (NZEB) concept. This study examines the impact of combining insulation retrofitting and photovoltaic (PV) system integration on reducing energy demand in existing buildings in Indonesia, considering both the 2024 and 2050 climate conditions. Three insulating materials with a thickness of 50 mm were selected, based on their thermal properties and material cost. The selected materials were rockwool, polyisocyanurate (PIR), and aerogel board as an internal layer of the walls. In addition, an economic evaluation was conducted to compare the cost-effectiveness of the three insulation materials, assessing not only energy savings but also payback periods. The results were obtained through U-value calculation, energy simulation, Photovoltaic (PV) panels simulation and economic evaluation. From the three selected insulation materials, PIR showed the highest energy demand reduction with a reasonable payback period of 15 years. Based on the simulation, PIR potentially reduced Energy Use Intensity (EUI) by 22.5% and 29.9% in the 2024 and 2050 climate database, respectively. PV panels, particularly the 300 Wp system with a shorter payback, covered an average of 31.5% of the building's final energy demands after adding PIR as an insulation in the 2024 climate database. The combined retrofit strategy reduced the overall payback period to 8.3 years. These findings highlight the NZEB approach as a viable pathway to support Indonesia’s energy-efficient building and renewable energy targets.

No abstract available

Modern office buildings with glass envelopes often encounter challenges in energy efficiency, particularly due to excessive reliance on artificial lighting during daytime and intensive use of cooling systems. This condition is occurred at the PT Pertamina Patra Niaga - ITJ Jakarta office building. The installation of window films and curtain blinds has made interior spaces darker, preventing optimal utilization of natural daylight and consequently increasing electricity consumption. Such conditions not only reduce energy efficiency but also compromise visual comfort for occupants. This study aims to analyze the impact of retrofit shading and the double skin façade (DSF) on daylighting performance, visual comfort, and energy efficiency. The research employed an existing condition analysis using the Sefaira plug in SketchUp software and Sefaira web model - energyplus to simulate and evaluate the effects of retrofitting on the glass office building. The findings reveal that the integration of retrofit shading and DSF significantly improves natural daylighting quality 2% underlit, 42% well lit, and 44% overlit. Visual comfort was enhanced by lowering indoor illuminance levels of ASE from 87% to 43% lux and sDA from 100% to 98% lux, aligning with recommended standards. Furthermore, annual electricity consumption decreased substantially, from 269 to 130 kWh/m² per year. In conclusion, retrofit shading and DSF provide effective passive design strategies that enhance daylight utilization, improve occupant comfort, and support energy conservation. This study serves as a preliminary investigation for future research on integrating multiple passive design strategies in office building retrofits.

Building archetypes are useful in building energy simulations as they simplify the modelling process. These building archetypes are classified in the Building Technology Assessment Platform (BTAP), a database built on Natural Resources Canada building codes. There are two groups: buildings established 1980 to 2004 and buildings established before 1980. The major drawback with the BTAP archetypes is that there are no considerations made regarding changes in mechanical systems in pre-1980 buildings, nor are the impacts of this evolution examined. This study expands the available archetypes by investigating typical heating, ventilation and air conditioning (HVAC) systems used for offices and multi-unit residential buildings in the City of Toronto by analysing data from municipal and industry partner sources to determine system characteristics for each building type for each period and suggest retrofits for the selected characteristics. This study identifies common building clusters based on building topology, size and vintage to develop more varied archetypes. By increasing the granularity of existing archetypes and presenting them for ASHRAE climate zone 5 A, retrofit modelling for Canadian buildings will improve in accuracy. Both baseline and retrofit conditions are modelled in both current and decarbonised thermal and electricity source conditions to understand the relative benefit of individual building vs district utility retrofits. Practice relevance This study furthers the applications of archetype development in North America by developing a set of granular HVAC system characterisations to better model existing buildings. This will support urban- and portfolio-scale energy modelling by enabling rapid simulation of existing buildings with increased accuracy versus existing ‘reference model’ methods.

The urgent need to decarbonize existing buildings has led to an increased focus on energy retro6its as a crucial strategy for improving building performance and sustainability. While building energy simulation software offers valuable insights into the effects of various retro6it scenarios, determining the optimal energy retro6it solution remains a signi6icant challenge due to the prohibitive computational costs associated with simulation-based optimization. This study presents an alternative approach to multi-objective design optimization of building energy retro6its involving a building energy simulation surrogate model. A case study of a religious building in Metro Manila, Philippines was used to demonstrate the proposed methodology. The methodology begins with creating a comprehensive building energy simulation database using Latin Hypercube Sampling, followed by training regression models for energy consumption and thermal comfort using this database. Next, these regression models were coupled with a Genetic Algorithm to perform multi-objective optimization. Finally, ranking of solutions in the Pareto front was demonstrated using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Optimization results were validated with the resulting accuracy and reliability of the surrogate model-based approach being within acceptable limits. The 6indings of this study suggest that regression surrogate models offer a computationally ef6icient and effective means of optimizing building energy retro6its. By providing a practical framework for multi-objective optimization, this research contributes to the advancement of sustainable building practices and supports the broader goal of decarbonizing the built environment.

Abstract With the vision of new urbanization and the continuous advancement of the renewal and renovation of existing urban areas and building energy efficiency improvement projects, large-scale energy-saving renovation of building clusters has become an important content of eco-friendly and low-carbon as well as an important means to improve the energy efficiency level of the region. This study proposes a building energy consumption information model adapted to data-driven analysis and a predictive stochastic model with an improved Monte Carlo method. Focusing on the calibrated estimation method based on Bayesian inference model, the estimation of the parameters to be estimated is generated by setting the prior distribution of the parameters to be estimated, deriving the posterior distribution, and simulating the Gibbs sampling based on the MCMC algorithm. The Monte Carlo method is applied to simulate and analyze the whole life cycle cost of the building complex, and the predictive stochastic model is also verified by the energy-saving renovation of the building complex. From the experimental results, it can be obtained that the total cost of the traditional house is 595,582 yuan, while the total cost of the eco-house is 564,251 yuan, i.e., compared with the traditional house, the eco-house can save more money in the process of using the house. In the validation comparison between the output of the prediction model and the actual data before and after remodeling, the average energy savings before and after remodeling both deviate by 22.22%, and the deviation of the energy saving rate is larger than the deviation of the total energy consumption. In summary, this paper proposes an improved predictive stochastic model of Monte Carlo method to provide theoretical guidance for the prediction, decision-making and optimization of energy-saving retrofit of building clusters.

In Europe, the buildings sector is responsible for 40% of energy use and more than 30% of buildings are older than 50 years. Due to ageing, a large number of houses require energy-efficient renovation to meet building energy performance standards and the national energy efficiency target. Although Swedish house owners are willing to improve energy efficiency, there is a need for a dedicated platform providing decision-making knowledge for house owners to benchmark their buildings. This paper proposes a data-driven framework for building energy renovation benchmarking as part of an energy advisory service development for the Västerbotten region, Sweden. This benchmark model facilitates regional homeowners to benchmark their building energy performance relative to the national target and similar neighbourhood buildings. Specifically, based on user input data such as energy use, location, construction year, floor area, etc., this model benchmarks the user’s building performance using two benchmark references i.e., 1) Sweden’s target to reduce buildings by 50% energy use intensity (EUI) by 50% by 2050 compared to the average EUI in 1995, 2) comparing user building with the most relevant peer group of buildings, using energy performance certificates (EPC) big data. Several building groups will be classified based on influential factors that affect building energy use. Hence, this benchmark provides decision-making supportive knowledge to homeowners e.g., whether they need to perform an energy-efficient renovation. In the future, this methodology will be extended and implemented in the digital platform to provide helpful insights to decide on suitable EEMs. This work is an integral part of project AURORAL aims to deliver an interoperable, open, and integrated digital platform, demonstrated by cross-domain applications through large-scale pilots in 8 regions in Europe, including Västerbotten.

The decision-making process to select solutions in residential building renovation projects is a complex task. These projects encounter rigid governance and ownership structures, participation of multiple stakeholders, diverse requirements, and a lack of consensus regarding methodologies and tools used within the decision-making process. Addressing some of these challenges requires a shared understanding of this domain. Therefore, this paper presents the Renovation-Decision-making (Reno-DM) model mapping key knowledge from the decision-making in renovation projects. It considers project characteristics, context information, building features, and their relation to aspects of the decision-making itself. The development of the Reno-DM model relies on an existing decision-making representation and the extraction of new concepts from interviews with stakeholders taking part in renovation projects and related literature. The interviews are analysed following an inductive content analysis approach. The Reno-DM model is developed in the form of an ontology. It provides a basis from which future knowledge management and reuse applications can be developed and deployed to support improvements in the decision-making process in the renovation field.

Applying circular economy principles to the renovation of existing buildings is increasingly recognized as essential to achieving Europe’s climate and energy goals. However, current decision-making frameworks rarely integrate life cycle carbon assessment with multi-criteria evaluation to support circular renovation strategies. This paper introduces an innovative framework that combines life cycle carbon assessment with multi-criteria decision analysis to identify and sequence circular renovation measures. The framework was applied to a residential case study in the Netherlands, using IES VE for operational carbon assessment and One Click LCA for embodied carbon assessment, with results evaluated using PROMETHEE multi-criteria analysis. Renovation measures were assessed based on operational and embodied carbon (including Module D), energy use intensity, cost, payback period, and disruption. The evaluation also introduced the embodied-to-operational carbon ratio (EOCR), a novel metric representing the proportion of embodied carbon, including Module D, relative to operational carbon savings over the building’s lifecycle. The homeowner’s preferences regarding these criteria were considered in determining the final ranking. The findings show that circular insulation options involving reused materials and designed for disassembly achieved the lowest embodied carbon emissions and lowest EOCR scores, with reused PIR achieving a 94% reduction compared to new PIR boards. The impact of including Module D on the ranking of renovation options varies based on the end-of-life scenario. The framework demonstrates how circular renovation benefits can be made more visible to decision-makers, promoting broader adoption.

The present paper describes a novel and user-friendly Decision Support System (e-DSS) designed to assist technicians in the preliminary design stage of a building renovation process based on the solutions developed in the innovation project e-SAFE, funded by the EU under the H2020 program. The e-DSS is engineered to rapidly assess key performance indicators, including energy performance before and after renovation, reduction in CO2 emission for space heating, space cooling, and DHW preparation, seismic upgrade feasibility, expected costs, and payback time. To demonstrate its capabilities, the e-DSS was applied to an existing public housing building in Catania, southern Italy. The predicted thermal energy needs for space heating and cooling were compared to the results from detailed simulations using a professional-grade software tool, for both as-built condition and a proposed renovation generated by the e-DSS itself. The discrepancies identified through this comparison will inform the refinement of the e-DSS algorithms to increase their accuracy and reliability. More generally, this paper recommends suitable algorithms that can be effectively employed in the development of simplified decision-making tools specifically tailored for building professionals operating in the early phase of building renovation projects.

No abstract available

With accelerating climate change and the urgent need to cut carbon emissions, global focus has turned to the existing building stock and its renovation. Sustainable renovation helps to achieve better energy performance and gain wider sustainability benefits, such as increased value of a building, improved indoor and outdoor comfort, reduced carbon emissions, and the higher satisfaction and better emotional state of inhabitants. Numerous systems and tools have been developed worldwide to assist with decision making in the choice of preferred modernisation scenarios and alternatives. However, social aspects are often neglected in the existing systems, and emotions of inhabitants are rarely analysed. To close this gap, the present study proposes an innovative decision-making framework for sustainable renovation solutions, based on emotion recognition. The framework makes it possible to assess various renovation alternatives against sustainability criteria and real-time measurements of the emotional states of inhabitants. Based on the proposed framework, an intelligent multi-criteria decision support system was developed by integrating COPRAS and the facial action coding system, the method of automatic facial expression recognition, and the continuous calibration and participant methods. The system was tested in the case study of renovation solutions for a building located in Ukraine. The research results revealed that the proposed renovation solutions had a positive impact on the emotional state of inhabitants, especially when visual materials such as drawings were presented. Some case studies were analysed together with the application of decision system tools and building information modelling (BIM) subsystem integration as a multidiscipline application of various applied sciences for representation and data analysis. The authors of this research have been analysing human emotional, affective and physiological states for many years and collected over a billion of these data in Vilnius city during the H2020 ROCK, SAVAS and BIM4REN projects. Data acquired during measurements in Vilnius were used to determine correlations and trends for the case study. The proposed methodology and findings of the study can be useful for researchers who use the evaluation and analysis of human emotions when there is a need to choose appropriate renovation measures or find alternative solutions.

In the early stages of building renovation projects, the lack of information on the existing building and an unclear definition of project objectives have been identified as critical bottlenecks. An early decision support tool has been developed to mitigate or overcome these barriers. The tool provides initial estimates tailored to the specific building and climate being assessed, and can be used to guide the renovation design at an early stage, even from very limited information. It includes the automatic generation of potential renovation scenarios, a dynamic energy simulation of existing and renovated cases, integrated economic and environmental assessments from a life cycle perspective, and a multicriteria analysis that ranks renovation alternatives to meet the interests of the stakeholders involved. The user obtains an initial indicative budget and the expected potential benefits of several renovation projects over comfort, energy consumption, economic cost and environmental performance. One of the main assets of the tool is the reduction in time and cost at initial phases of the renovation process, avoiding the need of site visits and the use of more laborious simulation tools.

In Germany, as in many developed countries, over 60% of buildings were constructed before 1978, where most are in critical condition, requiring either demolition with plans for redevelopment or renovation and rehabilitation. Given the urgency of climate action and relevant sustainable development goals set by the United Nations, more attention must be shifted toward the various sustainability aspects when deciding on a strategy for the renovation or redevelopment of existing buildings. To this end, this study focused on developing a smart decision support framework for aging buildings based on lifecycle sustainability considerations. The framework integrated digital technological advancements, such as building information modeling (BIM), point clouds processing with field information modeling (FIM)®, and structural optimization, together with lifecycle assessment to evaluate and rate the environmental impact of different solutions. Three sustainability aspects, namely, cost, energy consumption, and carbon emissions, were quantitatively evaluated and compared in two scenarios, namely, renovation, and demolition or deconstruction combined with redevelopment. A real building constructed in 1961 was the subject of the experiments to validate the framework. The result outlined the limitations and advantages of each method in terms of economics and sustainability. It was further observed that optimizing the building design with the goal of reducing embodied energy and carbon in compliance with modern energy standards was crucial to improving overall energy performance. This work demonstrated that the BIM-based framework developed to assess the environmental impact of rehabilitation work in aging buildings can provide effective ratings to guide decision-making in real-world projects.

The building sector contributes significantly to energy consumption and greenhouse gas emissions, with many buildings being energy inefficient. In response, the European Green Deal promotes improving energy efficiency to support decarbonization goals. However, managing energy consumption and integrating data from multiple sources presents challenges, especially for large building portfolios. This study introduces a novel methodology designed to optimize energy renovation strategies, balancing technical, financial, and maintenance considerations. The methodology is implemented in CERPlan 1.0, a web-based decision-support platform that combines data on building energy performance, renovation costs, and maintenance needs. Through simulations, CERPlan 1.0 helps decision-makers prioritize retrofit interventions based on economic criteria while leveraging synergies between energy improvements and regular maintenance. Application of this methodology to real estate portfolios reveals opportunities to enhance cost-effectiveness and energy savings. The results show that integrating maintenance into renovation planning reduces payback times and allows for more comprehensive renovation strategies. The conclusions highlight CERPlan 1.0’s potential to improve decision-making, making building renovations more efficient and sustainable.

One of the main challenges for architects and technicians is the efficient management of the built environment, in response to the growing deterioration and obsolescence in the building stock. This research introduces the design, development and application of a novel decision support system that assesses the multidisciplinary advantages or disadvantages of different intervention strategies, mainly focused on schools and social housing. The concept of the model aims to gather, interrelate and weight different renovation factors and variables, according to technical, social, energy and economic parameters, quantifying results on the impacts, consequences and benefits of each renovation strategy and providing practical outcomes in the design, construction, management and maintenance stages. This study uses schools and multi-family buildings, located in southern Europe, to apply and test the system iteratively in both building typologies, serving to adjust the calculation model and demonstrate its operation and replicability. The results are classified according to different intensity levels with their corresponding design alternatives along with a graphical output of results for decision-making. This model is expected to contribute to policy-making by introducing new theoretical and practical renovation guidelines, with a rational adjustment of proposals and ensuring effective and feasible action strategies in the built environment.

In recent years, discussions surrounding climate change have increasingly emphasized the significance of demand-side solutions. This shift has led to an interdisciplinary and bottom-up approach aimed at supporting global efforts to mitigate climate change. However, conventional modelling tools used to understand the energy demand system and to inform policymaking often fall short in capturing bottom-up dynamics accurately and at the required level of granularity. This is particularly evident in the nuanced and complex aspects of behavioural and social changes and their interactions. This research introduces a novel coupled -agent-based and integrated assessment modelling framework designed to analyse the advantages arising from diversity in renovation decisions, social dynamics, and the evolution of residential building stocks. This study demonstrates that, to effectively formulate realistic policies leading to substantial changes in building energy demand, policymakers require decision-support tools that extend beyond the confines of the rationality principle.

With global energy consumption increasing and environmental problems worsening, the need for effective solutions to reduce energy consumption in buildings is urgent. Energy renovation plays a key role in achieving this objective, allowing the energy efficiency of buildings to be improved and their negative impact on the environment to be mitigated. However, stakeholders involved in the renovation process often struggle to select the most appropriate energy efficiency measures to be implemented in a building. This paper aims to promote energy efficiency retrofits by providing an ML-based decision support system for identifying the most suitable and impact-heavy renovation actions for a certain building. First, an analysis of the available data from energy efficiency projects that have been implemented in the past is carried out and the different energy efficiency measures are identified. Then, different machine learning algorithms are applied, and the algorithms with the highest degree of accuracy are identified and further optimized. The results estimate the most energy efficient measures, in addition to the CO2 savings achieved. These results show how different combinations of features and algorithms can influence the outputs, leading to the selection of the best combination of techniques, indicating that Random Forest and LightGBM algorithms outperform other models in predictive accuracy, making them suitable for large scale energy management applications. Moreover, the results emphasise the importance of machine learning in the field of energy efficiency and energy in general, since the potential of machine learning models to support stakeholders in decision-making procedures are highlighted, taking advantage of existing data, conducting systematic analysis of available information and providing useful insights and predictions of the impact of specific interventions.

No abstract available

No abstract available

Building energy-saving design is significant for the industry to achieve carbon reduction and sustainable development. Firstly, a multi-objective model for energy consumption, cost, and carbon emissions is established based on the three-dimensional perspectives of society, nature, and economy. Then, a polynomial operator is used to improve the non dominated sorting genetic algorithm to calculate the optimal solution set. The low computational efficiency caused by direct coupling of algorithms in traditional optimization processes is expected to be addressed. According to the results, for the Square1 dataset and Iris dataset, the algorithm proposed in this study improved the reverse distance and convergence metrics by more than 70% compared to support vector machine-genetic algorithm and multi-objective clustering algorithm, with values closer to 0. The solution solved by this algorithm had lower building costs, energy consumption, and carbon emissions, with values of 345200 yuan, 2374 KWh/year, and 26 tons, respectively. This validates the effectiveness of the multi-objective model and solving algorithm established in the study, which helps to obtain the optimal energy-saving design scheme and provides reference for low-carbon optimization of building.

The increasing frequency of interior renovation and fit-out in office buildings raises concerns about indoor environmental quality, occupant health, and sustainability performance, yet existing certification systems remain largely design-stage or whole-building oriented and provide limited guidance for recurring renovation cycles. This study develops a health-oriented assessment framework for office interior renovation as a structured decision-support tool for practitioners and policymakers. We adopted an integrated approach combining a targeted literature review, expert consultation, the Fuzzy Delphi Method (FDM) for indicator screening, and the Analytic Hierarchy Process (AHP) for hierarchical weighting, based on an expert panel of 20 professionals spanning green building certification, architecture/interior design, MEP engineering, property/facility management, and energy/environmental consulting. Through consensus screening and weighting, four assessment dimensions and eighteen key indicators were identified and prioritized. Environmental quality was ranked highest (39.2%), followed by safety management (23.0%), functional usability (21.1%), and resource efficiency and circularity (16.7%). At the indicator level, indoor air quality management, Heating, Ventilation and Air Conditioning (HVAC) energy efficiency, space-friendly layout, preliminary assessment and planning, and thermal comfort emerged as the top priorities. Overall, the framework bridges the gap between certification-oriented evaluation and the operational realities of office renovation, enabling more consistent integration of health and sustainability considerations across renovation decision-making.

The urgency of renewing the Architecture, Engineering and Construction related processes to increase quality standards and performances while reducing costs and operations time is widely discussed in literature. In this scenario, increasing the energy renovation rate of the existing European building stock is a key priority to support the EU’s 2050 decarbonisation targets through innovative solutions. The introduction of prefabricated panels for building renovation – incorporating insulation, mechanical systems, and finishing – can provide the existing buildings with improved structural, thermal, acoustic, and architectural features. The higher quality and safety for the off-site activities, the faster on-site application and the reduction of waste are some advantages of this typology of Modern Methods of Construction (MMC). Several digital and informative tools have been introduced over the last years to customize and integrate the design of prefabricated panels on existing building envelopes (i.e. panelisation tools). However, the comparison of technological alternatives is left to the intuition of designers and managed through the use of several tools that are not interconnected and often downstream the design process. This paper presents a Panelisation Design Tool, which is a Decision Support System (DSS) to help decision-makers in the choice of technological solutions for retrofitting operations during the Early Design Stage. Thanks to BIM integration, some indicators related to different aspects (n Dimensions) are extracted from the model of the panelised building to compare different technologies in a systematic way. The Panelisation Design Tool is tested on a case study building located in the city of Monza, in Northern Italy, used as a pilot in the BIM4EEB European Project. The test aimed at demonstrating the effectiveness of the chosen parameters to evaluate multiple technological solutions in an integrated BIM approach.

Insulation materials represent the first and most important improvement measure when refurbishing residential buildings. Materials, however, differ on a wide set of criteria (e.g. functional, environmental, economic). It remains difficult to find trade-offs between these criteria in collective decision-making processes about the choice of renovation materials. Together with energy collectives and construction engineers, homeowners hence seek to find solutions that balance engineering evaluation methods and consumer preferences. This problem is addressed by the platform ROTUNDORO, which introduces a prototype online platform for engineers to simulate the effects of different material choices considering multiple criteria. ROTUNDORO relies on Building Information Modelling (BIM) and Linked Building Data (LBD) to link material performances to building components, which are then assessed and visualised in design performance scenarios. To verify these design scenarios, their potential market adoption is visualised inside the platform to show the probability of acceptance by the homeowners. The calculation of this probability of acceptance is based on consumer research, backed by the results of a Stated Choice Experiment (SCE) conducted amongst 500 Dutch homeowners, to investigate preferred choices between insulation material packages. Our findings reveal a high willingness of the studied population to invest in energy refurbishment. Reducing CO2 emissions and noise levels as well as improving comfort is just as important as financial savings.3 4 5

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The goal of this paper is to present a usable and effective tool to evaluate residential façade retrofitting solutions in early stages of design, keeping into account envelope features and installation issues. Decarbonisation goals set for 2050 impose existing building stock renovation and energy retrofit. Several drivers are available in EU Countries to trigger these operations. Nonetheless, the renovation rate in EU Member States remains low: barriers to building retrofit are identified, and a main issue in this sense is the lack of use of Decision Support Systems. DSS exist but are often neglected by building designers or owners, due to different reasons. Existing methodologies do not take into account the quantity and quality of information available at the various stages of building life cycle; furthermore, they mainly focus on energy related aspects, neglecting technological and installation related factors. This paper aims at providing an LCC-based decision framework to help decision makers in early stages of design to choose the most suitable technology for building façade retrofitting. A Utility Function expressing LCC for residential building renovation is provided, focusing on façades renovation and on installation and morphology related aspects. Information and data flow through the phases is presented and discussed, showing how the proposed method can be adapted to different stages, and testing its robustness through sensitivity and uncertainty analyses. Three main categories of renovation technologies are analysed (ventilated façade, ETICS, and prefabricated solutions). The proposed method is applied to a residential case study building. The adaptability of the tool to different stages of design is discussed, and further potential applications are presented.

This study investigates the deployment of VERIFY, a dynamic life cycle assessment (LCA) and life cycle costing (LCC) tool, tailored to evaluate the energy and environmental performance of building renovation strategies. The tool was applied to three diverse building renovation projects across Europe, offering insights into how life cycle-based tools can enhance decision-making by integrating operational data and modeling of energy systems. The paper highlights how VERIFY captures both embodied and operational impacts—addressing limitations of conventional energy assessments—and aligns with EU frameworks such as Level(s). Key findings from the case studies in Italy, Spain, and the Netherlands demonstrate how LCA/LCC-based approaches can support energy efficiency objectives and guide sustainability-aligned renovation investments. Across the three case studies, the tool demonstrated up to 51% reduction in primary energy demand, 66% decrease in life cycle greenhouse gas emissions, and 51% reduction in life cycle costs. These outcomes provide researchers with a validated dynamic LCA/LCC framework and offer practitioners a replicable methodology for planning and evaluating sustainability-driven renovations. Despite their advantages, the effective use of LCA tools in energy renovation faces challenges, including limited data availability, regulatory fragmentation, and methodological complexity. The paper concludes that advanced tools such as VERIFY, when harmonized with evolving EU energy performance and sustainability standards, can strengthen the evidence base for deep energy renovation and carbon reduction in the building sector.

Sustainable building management requires creative interpretation of direct user needs, a skilful balance between technological innovation and applied research into the concept of “Possible Quality”. Med-EcoSuRe research project proposes a pragmatic approach to innovation, whereby experimentation involving the active engagement of end users is conducted with particular focus on human-environment centred design. The objective of this approach is to disseminate effective energy efficiency strategies in university buildings through a cross-border Living Lab. Physical and virtual tools were implemented to foster dialogue and collaboration between academics, decision-makers and stakeholders, and to support university energy managers in planning and implementing innovative energy measures. This paper, starting from a rapid illustration of the results of the research project, illustrates the value enhancing actions post-closure of the project, in progress and/or planned.

The CALECHE project, fundSby Horizon Europe, is pioneering sustainable renovation practices for historic buildings that balance energy efficiency with heritage conservation. While this study primarily focuses on energy efficiency in the context of historic building renovation, it also considers aspects of holistic sustainability, such as the use of sustainable materials and adaptive reuse strategies. Through a comprehensive literature review, this study validates the CALECHE HUB conceptual model, emphasising multi-benefit decision-making processes. Key areas of focus includes the assessment of cultural and social values, economic benefits, and environmental sustainability. The analysis explores adaptive reuse strategies and decision support systems (DSSs) to harmonise heritage conservation with energy retrofitting. Using four European demonstration sites as a proof of concept, the findings highlight the need for holistic approaches that respect historic integrity while promoting contemporary functionality. The study underlines the urgency of bridging research gaps, particularly in the integration of new technologies and LCA, to ensure that historic buildings remain vibrant, sustainable, and socially inclusive assets for future generations.

To meet European carbon neutrality targets and comply with building energy performance regulations, renovating historic buildings is considered one of the most challenging tasks for the construction sector. On one hand, commonly employed renovation solutions are often more difficult to implement on these structures. On the other hand, renovation work must be carried out while preserving their heritage value and integrity. The European standard EN 16883 on conservation and energy renovation performance of cultural heritage was developed in 2017 with the aim of facilitating energy performance improvements in historic buildings while respecting their cultural significance. In pursuit of the same objective, the “P-Renewal” project focuses on the energy retrofit of pre-war Walloon housing with heritage value, providing valuable support through a reflective process and decision-making tools. These tools enable the selection of renovation strategies that effectively combine the preservation of heritage value with improvements in internal comfort, energy efficiency, and environmental performance. This study compares the reflective process of the research project with the guidelines of the standard EN 16883 and discusses the transferability of this contribution to other European contexts. This will significantly contribute to the evolution of energy performance standards for the renovation of historic buildings.

Old residential areas present unique challenges in terms of design, stakeholders, and renovation requirements compared to traditional building projects. However, unreasonable construction plans can lead to delays, cost overruns, poor quality, and conflicts between the construction party and local residents. This article proposes an optimization model that prioritizes progress, quality, and cost as the key control objectives, leveraging the actual conditions of renovating old residential areas. The NSGA-II genetic algorithm is employed to solve the mathematical model. To validate the effectiveness and scientific rigor of the algorithm, a renovation project in an old residential area in Wuhan is used as a case study. The findings of this study offer valuable theoretical support for decision makers in selecting appropriate construction plans.

The architectural, engineering, construction, and operation (AECO) sector is one of the main contributors to energy consumption and greenhouse gas emissions in Europe, making the renovation of the existing building stock a priority. However, defining effective and economically sustainable interventions remains a challenge, partly due to the variability of building characteristics and the lack of digital tools to support data-driven decision making. This research aims to identify the main factors influencing the energy consumption of buildings by analyzing a large database of building characteristics using machine learning algorithms. Based on the parameters that the analysis shows to have the greatest impact, the average cost of energy retrofitting measures will be used to elaborate a cost–benefit analysis model and the economic payback time for each measure, individually or in combination. The expected result is the creation of a tool that will allow the operator to evaluate the choice of interventions based on the energy efficiency that can be achieved and/or the economic sustainability. The proposed methodology aims to provide a digital approach that is replicable and adaptable to different territorial realities and useful for strategic planning of energy transformation in the building sector.

As the global initiative for carbon neutrality in the construction sector accelerates, the low-carbon retrofitting of existing buildings is emerging as a critical pathway to combat climate change. This paper proposes a systematic framework that integrates explainable machine learning with multi-objective optimization to support the sophisticated optimization of carbon emissions in renovation projects. The framework is centered on three core. Material Carbon Emission Intensity (MCEI), Operational Carbon Emission Intensity (OCEI), and Seasonal Carbon Emission Balance (SCEB). Leveraging high-resolution carbon emission simulation data, predictive models were developed using six machine learning algorithms, among which CatBoost demonstrated superior performance. Subsequently, SHAP values were employed to identify key design variables influencing carbon emissions, such as FLH, WWR1, NOF, and WWR2, thereby providing an evidence-based foundation for strategic decision-making. The framework’s utility was validated through a case study of a three-story industrial building retrofit, where the NSGA-II algorithm was applied for multi-objective optimization. This process yielded four distinct sets of feasible solutions. The most balanced solution achieved a 71.06% reduction in MCEI, a 37.20% reduction in OCEI, and a 24.75% improvement in SCEB compared to the baseline scenario. This study culminates in a series of recommended low-carbon strategies, including material reuse, promotion of low-carbon materials, optimization of partition walls, enhancement of the thermal performance of the building envelope, and improvements in atrium design. In conclusion, this research provides a systematic, scalable, and replicable technical pathway for the low-carbon retrofitting of buildings, holding significant practical value for achieving carbon neutrality goals.

As the world’s third-largest oil and natural gas producer, Iran consumed enormous amounts of non-renewable energy during the last twenty years. There are many obsolete buildings in the Iranian building stock, which required energy renovation. Many studies in the literature proposed energy retrofitting strategies to increase the efficiency of buildings, but few of them involve an energy network for the entire neighbourhood (such as district heating). Moreover, energy renovation is not sufficient to improve the smartness level of a community; in fact, it is essential to evaluate sustainable and social aspects. In this direction, this study aims to develop a comprehensive analysis of the current criticalities of a district in Tehran (District 5), proposing strategies to face the pollution of the city, provide a healthy environment for the citizens, and renovate the old buildings. The application of a decision support method is presented to set a priority ranking, pointing out the positive and negative impacts of each evaluated scenario. The energy renovation solution involved the installation of two storage tanks and solar collectors in each building and the connection with the district heating powered by waste to the energy plant. A multi-level car parking system and a noise mapping application were evaluated to solve mobility and pollution problems. Moving to the results, the priority ranking assesses that the most affordable action is the installation of a Solar Water Heater since energy and environmental indicators demonstrate its efficacy compared to the other solutions.

One of the sustainability goals in Europe is to reduce GHG emissions and energy consumption in buildings by 2030 and achieve climate neutrality by 2050. Renovation targets include a 16% reduction in primary energy use by 2030 in residential buildings and renovating 16% of least efficient buildings in the non-residential sector. Performance indicators in smart buildings are crucial for evaluating system performance objectively, aiding decision-making, and ensuring accountability. They provide insights into energy consumption, indoor air quality, and resource usage, facilitating cost reduction and enhancing user experience. These indicators also support compliance with regulations, certification standards, and sustainability goals. This work, under the umbrella of the DigiBUILD project, applies multi-dimension key performance indicators across ten European pilots to make better-informed decisions, improve data quality, and support policy-making for sustainable building infrastructure.

In historical district at European cities it is a major problem how to take decision on renovating or replacing existing buildings. This problem is imminent in Budapest (Hungary) in many traditional districts such as the Ferencvaros district where we selected a compound area for further examination. By financial aid for the renovation of these buildings which awarded by Municipal Assembly of this district in question there is much uncertainty and confusion concerning how to decide whether or not and how to reconstruct a building where new private owners apply for support. In this paper we propose a formal evaluation method based on fuzzy signature rule bases (the formal being a special case of L-fuzzy object). Using the available expert knowledge we propose a fuzzy signature model including relevance weights and weighted aggregations for each node and parent node, respectively, so that as a result a single membership value may be calculated for each building in question. Linguistic labels for decision (such as worthless, average, highly valuable, etc.) are generated from the values thus obtained. Such linguistic calculations might be of help for the Municipal Assembly awarding financial support. A complete example wit 26 buildings is presented.

Housing plays a key role in the world path to energy transition, and retrofitting buildings is a major asset to this end. Unfortunately, despite the supporting measures and incentives promoted in many countries, the renovation rate is still too slow. This is even more complex within some specific assets, such as social housing, which, especially in Italy, depends on the availability of public funds. The study proposes a predictive tool conceived as an enabler in the decision-making process, capable of considering and comparing the performance levels that different retrofitting actions can reach, according to building features, intervention costs, timing, and resource availability. The tool is tested on a social housing case study in Bologna.

In Europe, about 40% of the final energy consumption and 30% of the total CO2 emissions are attributed to the operation of buildings. National energy and climate plans focus on large scale implementation of different renovation scenarios in existing buildings to meet the 2030 and 2050 targets for energy efficiency and decarbonization. Building stock modeling (BSM) offers a useful tool for the assessment of scenarios to support decision makers in setting up effective strategies for meeting European and national targets. One of the challenges faced for the reliable application of this modeling approach is the limited availability of detailed data for the investigated stocks. In the framework of the European project – EIFFEL there is ongoing work to complement data from Earth Observations with in-situ and statistical data from different sources (e.g. Hellenic Statistical Authority, energy performance certificate registry) in order to derive a bottom-up BSM of increased reliability for applications in medium scale (municipality) to large scale (prefecture) areas. The enhanced BSM is based on the representation of the entire building stock by a set of building types defined in line with the TABULA methodology according to their use, size and age of the buildings. The results presented herein come from a pilot application for a municipality in northern Attica.

Energy audits are key to improving energy efficiency and supporting decarbonization in buildings. Traditional audits rely on static data, manual inspections, and simple assumptions, which can limit their accuracy and usefulness. This study introduces an innovative energy audit framework that combines Building Information Modelling (BIM), Internet of Things (IoT) monitoring, and multi-criteria decision analysis (MCDA) to enhance the reliability and effectiveness of building energy assessments. The proposed methodology combines BIM-based extraction of geometric and thermophysical building parameters with real-time operational data from IoT sensors. Key energy performance indicators, including specific energy consumption, potential energy savings, investment cost, payback period, and CO₂ emission reduction, are systematically evaluated. To support rational prioritization of energy efficiency measures, a weighted multi-criteria decision approach is applied, enabling transparent and reproducible ranking of retrofit and operational improvement options. The results show that integrating digital building models and continuous monitoring significantly reduces uncertainty in energy performance evaluation compared to traditional audit methods. Control-oriented measures, such as HVAC optimization and intelligent lighting systems, offer the highest short-term benefits, while envelope retrofitting provides substantial long-term energy savings. The proposed framework offers a scalable, data-driven solution for modern energy audits and supports the transition toward smart, energy-efficient, and sustainable buildings.

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The operation of Heating Ventilation and Air Conditioning (HVAC) systems in densely occupied spaces results in considerable energy consumption. In the post-pandemic context, stricter indoor air quality standards and higher ventilation rates further increase energy demand. In this paper, the energy retrofit of a partial recirculation all-air HVAC system serving a university lecture room located in Southern Italy is analyzed. Multi-Objective Optimization (MOO) and Multi-Criteria Decision-Making (MCDM) approaches are used to find optimal design alternatives and rank these considering two different decision-makers, i.e., public and private stakeholders. Among the Pareto solutions obtained from optimization, the optimal alternative is identified, encompassing three Key Performance Indicators and using a new robust MCDM approach based on four methods, i.e., TOPSIS, VIKOR, WASPAS, and MULTIMOORA. The results show that, in the post-pandemic era, baseline retrofit scenarios for infection reduction that do not involve the introduction of demand control ventilation strategies cause energy consumption to increase from negligible values up to 59%. On the contrary, baseline retrofit scenarios involving demand control ventilation strategies cause energy consumption to decrease between 5% and 38%. The findings offer valuable guidance for HVAC system retrofits in higher education and similar buildings, emphasizing the potential to balance occupant health, energy efficiency, and cost reduction. The results also highlight significant CO2 reductions and minimal impacts on thermal comfort, showcasing the potential for substantial energy savings through targeted retrofits.

A large-scale home energy efficiency (HEE) retrofit programme is required to reduce the UK building stock ’ s carbon footprint. However, retrofits that lack adequate ventilation can deterio-rate indoor air quality and result in adverse health effects. Research shows trickle vents (TrVs), recommended for installation following retrofit and presumed to remain open under the new Approved Document F (ADF) schema, are often found closed in homes. This paper quantifies the impacts of HEE retrofit and TrV use on indoor pollutant exposure and overheating risk, as one of the earliest research efforts to evaluate the potential impacts of HEE measures on the indoor environment following the latest ADF. A novel thermal-IAQ co-simulation technique using EnergyPlus and CONTAM was applied to archetypical models with different physical and environmental characteristics: a terraced house in London and a bungalow in Plymouth. The analysis considers exposures to indoor radon, formaldehyde, fine particulate matter (PM 2.5 ), nitrogen dioxide (NO 2 ), and overheating risk. Results reveal that the effect of HEE retrofit depends on pollutant type and TrV operation. Radon and formaldehyde exposures reduce post-retrofit when TrVs are continuously open but rise when kept closed. In contrast, HEE measures marginally increase PM 2.5 exposure but reduce NO 2 exposure when TrVs are open, while slightly increasing exposures to both pollutants when TrVs are closed. Furthermore, HEE retrofit without adaptation can escalate overheating risk. These findings underscore the importance of considering both HEE retrofit strategies and TrV use to mitigate indoor pollutant exposure and overheating in UK homes.

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Conventional approaches to building energy retrofit decision-making struggle to generalize across diverse building characteristics, climate conditions, and occupant behaviors, and often lack interpretability. Generative AI, particularly Large Language Models (LLMs), offers a promising solution because they learn from extensive, heterogeneous data and can articulate inferences in transparent natural language. However, their capabilities in retrofit decision-making remain underexplored. This study evaluates six widely used LLMs on two objectives: determining the retrofit measure that maximizes CO2 reduction (a technical task) and minimizes the payback period (a sociotechnical task). We assessed performance across accuracy, consistency, sensitivity, and reasoning. The evaluation used 400 residential buildings from a nationwide, simulation-based database. The results reveal that LLMs vary across cases, with consistently strong technical-task performance but notably weaker performance on the sociotechnical one, highlighting limitations in handling complex economic and contextual trade-offs. The models consistently identify a near-optimal solution for the technical task (Top-5 accuracy reaching 92.8%), although their ability to pinpoint the single best option is limited (Top-1 accuracy reaching 54.5%). While models approximate engineering logic by prioritizing location and geometry, their reasoning processes are oversimplified. These findings suggest LLMs are promising for technical advisory tools but not yet reliable for standalone retrofit decision-making.

ABSTRACT Despite a step change in the social-rented sector in recent years, retrofitting homes in the owner-occupied sector is still slow and expensive. This applies particularly to the case of solid wall (and unfillable cavity wall) homes where staple energy efficiency measures cannot be applied easily. Drawing on interviews with owner occupiers of traditionally constructed dwellings who have completed or are in the process of retrofit projects, this paper explores the role of time and cost within one-off energy retrofit programmes. It is observed that a mix of one-go and long-term projects of varying depths are being completed. Projects completed in one go appear most likely to achieve a deep retrofit required for meeting emissions reduction targets, yet the associated costs remain significant and beyond affordability for most households. Opportunities for time and cost savings are identified in the combination of renovation and retrofit works and the use of government subsidies, however, the correct sequencing of these measures is essential to avoid the occurrence of shallow retrofits or investment lock-ins.

This study proposes a sustainable multi-criteria optimization framework for the energy retrofit of collective residential buildings in Algeria, particularly those constructed between the 1970s and 1980s. Through on-site surveys, energy consumption analysis, and seasonal temperature measurements, the high energy demand of these buildings was confirmed. Using EnergyPlus simulations based on Meteoblue weather data, 16 retrofit strategies were assessed—incorporating various insulating materials applied internally or externally (via rendering or cladding). The ELECTRE III decision-making tool was employed, supported by the Simos Revised Framework (SRF) for weighting environmental, economic, and social criteria. Results demonstrate that all strategies significantly reduce energy demand—by up to 72.5%, with reductions reaching 94.4% in winter and 43.5% in summer, depending on insulation type and placement. Improvements in indoor thermal comfort were also observed, with exterior insulation beneath cladding offering the best performance during winter, while exterior rendering also proved effective in the summer. The ELECTRE III analysis identified rock wool and polyurethane with fiber cement cladding as optimal insulation solutions. The proposed approach supports national energy policies and aligns with the Sustainable Development Goals (SDGs), offering a replicable model for large-scale building retrofits in similar climatic and architectural contexts.

To evaluate the effects of different energy retrofit scenarios on the residential building sector, in this study, an urban building energy model (UBEM) was developed from open data, calibrated using energy performance certificates (EPCs), and validated against hourly electricity use measurement data. The calibrated and validated UBEM was used for implementing energy retrofit scenarios and improving the energy performance of the case study city of Varberg, Sweden. Additionally, possible consequences of the scenarios on the electricity grid were also evaluated in this study. The results showed that for a calibrated UBEM, the MAPE of the simulated versus delivered energy to the buildings was 26 %. Although the model was calibrated based on annual values from some of the buildings with EPCs, the validation ensured that it could produce reliable results for different spatial and temporal levels than calibrated for. Furthermore, the validation proved that the spatial aggregation over the city and temporal aggregation over the year could considerably improve the results. The implementation of the energy retrofit scenarios using the calibrated and validated UBEM resulted in a 43 % reduction of the energy use in residential buildings renovated based on the Passive House standard. If this was combined with the generation of on-site solar energy, except for the densely populated areas of the city, it was possible to reach near zero (and in some cases positive) energy districts. The results of grid simulation and power flow analysis for a chosen low-voltage distribution network indicated that energy retrofitting of buildings could lead to an increase in voltage by a maximum of 7 %. This particularly suggests that there is a possibility of occasional overvoltages when the generation and use of electricity are not in perfect balance.

Energy performance improvements in existing homes play a substantial role in the achievement of the UK’s net-zero emissions target. However, retrofitting dwellings remains a particularly challenging task in the UK, where traditional dwellings make up a large part of the building repository. Traditional dwellings’ contribution to decarbonization has not yet been fully realized due to the risks imposed to the thermo-hygrometric balance of their constructions and to their heritage value. These tend to hinder the “fabric-first” approach for the retrofit of such dwellings, where active measures are often prioritized. The aim of this research is to propose a systemic approach to intervene in Traditional Listed Dwellings (TLDs) to improve their energy performance by means of passive retrofit measures and to shape a more future-proof heritage. A mixed methodology was developed that utilizes 19th C TLD case studies (CSs) in South-East England and dynamic energy simulation (DES) to investigate their current energy performance and possible improvements using responsible, safe and effective energy retrofit scenarios. Providing an overview of the methodology adopted in this research, this paper presents the main results of this study. This paper highlights the savings associated with the best-performing combinations of retrofit measures and the areas of intervention where the highest energy and carbon savings can be achieved.

The real estate sector is steadily moving towards zero-emission buildings, driven by EU policies to achieve near-zero energy (NZEB) buildings by 2050. In Italy, more than 70% of residential buildings fall into the lower energy classes, and this mainly affects low-income households. As a result, the decarbonisation of the real estate sector presents both technical and socio-economic obstacles. Building on these premises, this study introduces the Retrofit Optimization Problem (ROP), a methodological framework adapted from the Multidimensional Knapsack Problem (MdKP). This method is used in this study to conduct a qualitative analysis of accessibility to retrofit between different socio-economic groups, integrating constraints to simulate restructuring capacity based on different incomes. The results show significant disparities: although many retrofit strategies can meet regulatory energy performance targets, only a small number are financially sustainable for low-income households. In addition, interventions with the greatest environmental impact remain inaccessible to vulnerable groups. These preliminary results highlight important equity issues in the energy transition, indicating the need for specific and income-sensitive policies to prevent decarbonisation efforts from exacerbating social inequalities or increasing the risk of assets being stranded in the housing market.

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Urban areas are responsible for most of Europe’s energy demand and emissions and urgently require building retrofits to meet climate neutrality goals. This study evaluates the energy efficiency potential of three public school buildings in western Macedonia, Greece—a cold-climate region with high heating needs. The buildings, constructed between 1986 and 2003, exhibited poor insulation, outdated electromechanical systems, and inefficient lighting, resulting in high oil consumption and low energy ratings. A robust methodology is applied, combining detailed on-site energy audits, thermophysical diagnostics based on U-value calculations, and a techno-economic assessment utilizing Net Present Value (NPV), Internal Rate of Return (IRR), and SWOT analysis. The study evaluates a series of retrofit measures, including ceiling insulation, high-efficiency lighting replacements, and boiler modernization, against both technical performance criteria and financial viability. Results indicate that ceiling insulation and lighting system upgrades yield positive economic returns, while wall and floor insulation measures remain financially unattractive without external subsidies. The findings are further validated through sensitivity analysis and policy scenario modeling, revealing how targeted investments, especially when supported by public funding schemes, can maximize energy savings and emissions reductions. The study concludes that selective implementation of cost-effective measures, supported by public grants, can achieve energy targets, improve indoor environments, and serve as a replicable model of targeted retrofits across the region, though reliance on external funding and high upfront costs pose challenges.

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Reflective materials, characterized by high albedo and thermal emissivity, offer effective passive cooling strategies for reducing building energy demand. While prior studies have developed thermal transfer models validated under laboratory conditions or conducted short-term monitoring in non-air-conditioned spaces, their effectiveness in operational buildings remains underexplored. This research evaluates the change in cooling energy demand and indoor thermal comfort in a retrofitted office building with reflective materials in China’s Hot Summer and Cold Winter (HSCW) zone. The calibrated WUFI®Plus simulations show that the application of reflective roof and window materials can result in an 11.3% reduction in cooling energy demand. Moreover, occupant surveys indicate improved thermal perception, with the mean Thermal Comfort Vote (TCV) rising from −0.75 to −0.30, thermal acceptability increasing from 0.10 to 0.35, and 80% of occupants reporting cooler conditions. These subjective results align with simulated Predicted Mean Vote (PMV) reductions (0.82 → 0.74), confirming the retrofit’s effectiveness. While the energy savings are more modest than those reported in Mediterranean climates, they are generally consistent with the energy saving ratios of buildings in the HSCW region as evaluated by previous studies. This study provides a framework for assessing retrofits in occupied buildings with reflective materials and indicates the practicality of such retrofits as an economic, low-disruption strategy for upgrading aging office building stocks in the HSCW zone.

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ABSTRACT This study aims to optimize the nearly zero energy renovation of rural houses in Xuzhou by exploring the appropriate range of design parameters for energy-efficient retrofitting. The research focuses on optimizing the envelope parameter ranges under nearly zero energy building (nZEB) targets. Specifically, a multi-objective optimization approach is employed to refine the envelope design parameters of rural dwellings in Xuzhou, with the goal of minimizing total building energy consumption, annual heating demand, and annual cooling demand.To ensure that the selected parameters align with the characteristics of rural residences in Xuzhou and comply with the nZEB standards in China, a baseline model was developed using actual field data and previous research findings. The model was constructed within the Rhino-Grasshopper visual programming environment, integrated with the Ladybug and Honeybee environmental simulation plugins. The Wallacei evolutionary optimization engine was applied to conduct multi-level simulations and perform automated searches for optimal envelope parameters, aiming to achieve minimal energy consumption.The optimization results demonstrate that the refined envelope design parameters lead to a 65.91% reduction in total building energy consumption, a 46.01% decrease in annual heating demand, and a 62.69% reduction in annual cooling demand. Additionally, the required renewable energy utilization rate increases to 46.22%. The post-optimization performance indicators fully meet the Chinese standards for nearly zero energy buildings. The optimized parameter ranges can serve as a valuable reference for the energy retrofitting of rural housing in Xuzhou and other cold regions of China. HIGHLIGHTS Based on Rhino-Grasshopper and the Ladybug & Honeybee plug-in, a benchmark model of rural residences in Xuzhou was constructed, and comprehensive energy consumption analysis was carried out. Through the multi-objective optimization algorithm and Wallacei optimization algorithm engine, building energy consumption, annual heating demand, and annual cooling demand were simulated and analyzed, achieving automatic optimization search for the design parameters of the rural residential envelope. The range of envelope parameters for rural residential buildings in Xuzhou to reach the standard of nearly zero energy consumption is clarified. Summarizing the optimization measures for nearly zero energy consumption retrofitting of rural residences in Xuzhou provides a reference basis for the energy-saving retrofitting of rural residences in other regions with cold climates.

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Building retrofits are strongly dependent on the individual refurbishment/rehabilitation cycles of buildings. In order to achieve the targeted climate goals for the building sector, it is necessary to make the best possible use of refurbishment opportunities for energy retrofits. Furthermore, it must be considered that the younger the building, the lower the potential for energy savings. The most challenging, yet required, energy retrofits are all yet to come. Given the importance of the refurbishment cycle, the question arises as to what the theoretical refurbishment rate is, what the actual energy retrofit rate is, and what it needs to be in order to meet the climate targets for the residential building stock. The aim of this paper is to model the evolution of the size and retrofit status of national building stocks. This is to be done against the background of the deviation from the actual and theoretical refurbishment cycle. Using current statistics and the past development of the German residential building stock, central parameters like living space, new construction, deconstruction, and the retrofit rate are modeled endogenously. For the latter, influences of economic conditions are implemented through an exogenously set black box variable. The retrofit rate represents a deep energy retrofit and thus allows an easy connection of the model with energy parameters for building classes defined in the web database TABULA for 20 European countries. The results are of relevance for modeling the energy efficiency status of the building stock and deriving suitable conditions for increasing retrofit rates also in other countries. The results show that increasing the energy retrofit rates to 2%/a or more is largely unrealistic. Instead, the focus should be on weak points, especially where short-term savings are concerned. This applies in particular to facades, basement ceilings, and heating systems as well as the efficient use of energy in buildings.

European directives on sustainable finance identify binding guidelines in investment planning for the decarbonization of the real estate sector. The Sustainable Finance Disclosure Regulation regularizes the methods of economic evaluation of projects by identifying the pillars Environmental (E), Social (S) and Governance (G) as thematic reference stylistic features. The ESG triptych constitutes inspiration in the scientific literature in the field for the proposal and testing of valuation algorithms aimed at the optimal structuring of investment portfolios. The paper proposes an ESG-based economic-financial analysis model for energy retrofit programs referring to the existing real estate sector. The model assumes the configuration of a multi-objective system built by borrowing algebraic formalisms of Operations Research, especially those of optimization algorithms. These algorithms make it possible to construct logical-functional relationships such as to represent the anatomy of the proposed evaluation model in terms of objective function and constraints, for example on the European decarbonization pathway target, or even on the available budget. The implementation of the proposed model, applied to a case study, returns a time priority list of assets to be energy efficient, balancing for each the investment costs, payback period and post-retrofit CO2 production.

Strong synergies exist between co-generation and renewables for reducing emission of harmful gases in the transition period towards carbon neutrality. Integration of CHP with renewables and energy storage along with electrification of buildings is a pathway to create resilient and efficient solutions towards decarbonization. Optimizing the size of a CHP system to satisfy both electrical and thermal loads of a given facility while minimizing fuel consumption is a challenging problem. The thermal load consists of heating, cooling, or hot water production. Integration of renewable energy sources and energy storage presents additional design challenges. An interactive software was developed by the authors to quickly estimate the performance of different configurations of CHP and renewable energy sources and it was tested for an office complex of 4 commercial buildings found in NREL's dataset End-Use Load Profiles for the U.S. Building Stock. The software is intended to size CHP devices and calculate the Energy Utilization Factor (EUF), CO2 emissions and the rate of Entropy Production. A heat pump module and solar PV with electrical power storage were introduced for this study. The objective of this paper is to compare different configurations of renewable or low emissions technologies and design an appropriate solution for a test data set for a Typical Metrological Year (TMY). The software requires energy inputs separated by cooling load, heating load and electric demand as a time series and is used to optimize the plant size to match all the loads to the corresponding systems.

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The current trend in renovating existing buildings is to perform retrofits on a case-by-case basis without a systematic assessment, using static tools with broad assumptions and generic inputs. As a result, only about 1% of the building stock undergoes energy renovations each year. To address this issue, new approaches and modern tools are necessary to enhance and expedite energy retrofits in Danish buildings. While there were a few initiatives and projects exploring the implementation of digital twins in building applications, the focus is primarily on newly constructed, highly energy-efficient buildings with integrated building information models (BIM). Conversely, existing and older buildings often lack any form of digital modeling, making it challenging to implement digital twins in those contexts. This paper presents an innovative digital twin solution, ‘DanRETwin’, which will provide decision-making support, retro-commissioning, and data-driven performance optimization for non-residential existing buildings. The proposed solution will utilize building operational data, employing machine learning and artificial intelligence techniques to develop scalable data-driven models of building energy. Additionally, clamp-on IoT sensors will be used for data collection, enabling a fully automated and flexible solution. By utilizing DanRETwin, building owners will enjoy higher energy efficiency and improved comfort in their retrofitted buildings; facility managers will have an advanced monitoring solution that enables systematic retro-commissioning of their newly retrofitted buildings, eliminating faults and reducing losses; consultants will have a potential solution to retrofit, enhance, and optimize their clients’ building performance, allowing them to make informed, data-driven decisions and interventions; and city planners will have an effective, scalable, and adaptable tool to expand retrofit efforts and evaluate various scenarios.

The increasing urgency to reduce greenhouse gas emissions has positioned the decarbonization of the existing building stock as a pivotal strategy. It plays a crucial role in the global transition towards forms of low‐carbon economies. This research proposes an integrated and replicable methodological framework designed to steer strategic investment decisions across the energy retrofitting initiatives field by harmonizing economic, environmental and technical dimensions. The methodology integrates established instruments from scientific literature, such as the Carbon Risk Real Estate Monitor (CRREM) for environmental risk evaluation, two‐dimensional scatter plots for exploratory analysis of trade‐offs and multiobjective optimization models based on goal programming principles. It has been applied to a diversified portfolio of 13 buildings located in the city of Rome (Italy). The results highlight the framework's capacity to identify optimal retrofit scenarios that balance investment expenses, payback periods and the alleviation of asset‐stranding issues. Furthermore, the sensitivity analysis outlines the adaptability of the method to different priority weightings, facilitating decision makers in effectively managing complex multicriteria trade‐offs. The study underscores the relevance of integrating environmental and economic criteria to foster investment strategies aligned with ESG standards, supporting both public administrations and private investors in achieving climate objectives.

Achieving a zero-emission building heating sector requires numerous strategies and detailed energy planning, in order to identify the optimal decarbonisation pathway. This work aims to assess the impact of district heating expansion and the implementation of energy-saving measures on the decarbonisation of the Italian building stock by 2050, analysing their combined impact, reciprocal effects, and technical–economic implications on the entire national energy system. The scenarios have been implemented and simulated with the H2RES software, a long-term energy planning optimisation model, built for the Italian national energy system. Results indicate that it is possible to decarbonise the heating system in an efficient and cost-effective manner by the year 2040. Heat pumps represent the optimal technology at both centralised and decentralised levels. District heating expansion is a priority for the decarbonisation of the building stock, allowing us to reduce costs, exploit thermal storage systems and provide system flexibility. In the best scenario, 40% of the Italian heat demand can be supplied by fourth-generation district heating. Energy-saving measures can reduce heat demand and primary energy but at higher annual costs and with a significant increase in investment. The combined simulation of the strategies within an optimisation model of the entire energy system enables the accurate assessment of the real impact of the various measures, considering their reciprocal effects and technical–economic implications.

The study has as its original database the decarbonization process initiated in Mexico by the National Commission for the Efficient Use of Energy (CONUEE) as part of its “Savings Program of Electric Power in Buildings of the Federal Public Administration” (PAEIAPF) of 1999. The primary purpose of PAEIAPF was to reduce the levels of electric power consumption in Federal Government buildings. The program has operated for 20 years; however, its scope only reaches operational carbon. Since 90% of existing buildings will be in use by 2050, the Retrofit Models will be the base to determine solutions for a more resilient living environment that fortifies and extends the grid’s capacity and meets climate change mitigation targets. Secondly, significant socioeconomic and profound environmental impacts are not calculated explicitly in existing tools and are often referred to as “secondary” or Non-Energy Benefits (NEB). “The goal is to give them a measurement value to be considered in the decision-making calculus. It is assumed that soon; such factors will enter the general climate change economy, not unlike carbon in the past decade.” In this context, the proposed research aims to develop a value-based framework that will support a Building Stock Model and subsequent Retrofit Models, documented in a web-tool platform. The framework has three main steps:A) Building Stock Model: Mapping of selected buildings of the program PAEIAPF in a GIS system. Documentation of the baseline energy consumption and embodied CO2-eq of the existing building.B) Retrofit Models: Involving a Whole Life Cycle Assessment (WLCA) and Non-Energy Benefits (resilience coefficient, health, productivity).C) Web Tool Platform: Application and toolset that allows for consistent documentation, environmental impact evaluation of existing building stock, and solution design in identifying energy reduction concepts.

Urban buildings consume raw material and energy, and they produce waste and greenhouse gasses. Sustainable urban development strategies aim to reduce these. Using the case study of buildings in Vienna, this article evaluates the impact of a defined urban development pathway on the heating energy demand, greenhouse gas emissions, and total material requirement of buildings in Vienna for 2021–2050. Furthermore, the impact of recycling to reduce the total material requirement and to increase the circular material use rate is evaluated. The results show that the heating energy demand can be reduced to meet the targets of Vienna’s sustainable development strategy. The same does not count for greenhouse gas emissions. To meet the targets for the latter, the renovation of old buildings by thermal insulation should be expanded and heating systems substituted. With respect to the total material requirement, the recycling of demolition waste from buildings in Vienna to produce secondary raw materials for buildings in Vienna can help to achieve the reduction targets of Vienna’s sustainable development strategy so that in the year 2050, the material footprint is only 44% of the value of the year 2019. Since there is a contradiction between the total material requirement and the circular material use rate, the latter has to be discussed for its use as a circular economy indicator, since the aim of circular economy is not to produce as much recycling materials as possible, but to reduce resource consumption to a sustainable level.

Building a reliable energy model for old residential buildings with insufficient documentation and user assistance is a challenging and time-consuming task. Nevertheless, the ambitious European decarbonization targets require this building stock to be renovated, making energy assessment a key priority. In line with this goal, the following study explores a more simplified and automatic framework to generate a residential building energy model (BEM). The paper’s approach is based on the concept of urban building energy modelling (UBEM) archetypes or building prototypes and is customized according to the principles of dynamic simulations performed in the existing BEM software, Integrated Environmental Solutions Virtual Environment IES VE, and Solemma Open Studio. Therefore, based on three real starting inputs, a prototype database (DB) of assigned inputs is generated, i.e., an input matrix, using Google Maps as a geometry source. Other data are drawn from tabular DB. The proposed approach is evaluated by benchmarking the simulation results with precise models and monitoring the data that come from the Horizon2020 project REZBUILD. Nevertheless, a level of simplification is introduced that creates less accurate results for total or system-level energy consumption; this is compensated for using a set of simple calibration steps. The approach gives promising results for daily indoor temperature, making it a suitable indicator for evaluating further retrofitting alternatives.

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This study proposes a stock-level method combining Consequential Replacement LCA (CRLCA) and range-bound analysis to assess the carbon payback time (CPBT) of large-scale construction strategies. Extending CRLCA beyond individual cases enhances representativeness and policy relevance. Leveraging stable stock-level population dynamics, the study introduces a per capita metric alongside the conventional per area approach, highlighting the individual dimension of carbon impact. Against the backdrop of Switzerland’s inward development policy, the study examines 335 residential building replacement (BR) projects in Zurich (2001–2019), compared with two assumed refurbishment-based alternatives. Results show a CPBT of approximately 16 years per area and 21 years per capita for BR under the most optimistic carbon assumptions. Even in this best-case scenario, BR fails to achieve payback for post-2019 decisions, as decarbonization reduces new buildings’ operational advantage. This pattern holds for public buildings, where BR performs even worse. Per capita metrics yield more conservative outcomes than per area metrics under identical conditions, underscoring the importance of metric choice and its role in linking individual accountability to more inclusive, citizen-informed policymaking. Thus, this study supports prioritizing refurbishment over replacement as a climate-conscious urban renewal strategy and stresses that construction strategies are time-sensitive to decarbonization and must be reassessed as conditions evolve.

The urgency of decarbonizing the built environment requires precise modeling of building stock energy performance for effective large-scale planning and retrofitting. Despite advancements in data and modeling techniques, uncertainties persist in balancing model complexity and accuracy, especially in representing occupancy patterns and their impact on energy demand at district and urban scales. This study examines various approaches to building stock energy simulation and occupancy modeling for district-level heating and cooling energy demand, using 19 buildings at a Central London campus as a case study. Five scenarios were evaluated: Scenario A employs THERMOS, a data-driven approach; Scenario B uses a single dynamic thermal simulation model for the entire inventory; Scenario C applies a thermal model with a uniform occupancy schedule across all buildings; Scenario D uses a thermal model with five distinct occupancy profiles; and Scenario E assigns unique occupancy profiles based on energy use data. Results showed that Scenario E ’ s annual heating demand estimation closely matched metered data (12 % difference), while Scenario A underestimated by 44 %. Complex occupancy models improved peak heating load predictions, with Scenario E showing only a 4 % difference from metered data, though it may not always be feasible due to data and computational constraints. Scenario D emerged as a promising balance between accuracy and efficiency. For cooling demand, significant differences among scenarios (56.43 to 6.1 kWh/m 2 /Y) underscored the importance of accurate occupancy modeling. This research identifies the optimal balance between model complexity and prediction accuracy, introduces the Energy Data-Driven Occupancy Schedule (EDDOS) method, and highlights the potential of data-driven approaches to enhance energy demand assessments.

Buildings play a key role in the transition to a low-carbon-energy system and in achieving Paris Agreement climate targets. Analyzing potential scenarios for building decarbonization in different socioeconomic contexts is a crucial step to develop national and transnational roadmaps to achieve global emission reduction targets. This study integrates building stock energy models for 32 countries across four continents to create carbon emission mitigation reference scenarios and decarbonization scenarios by 2050, covering 60% of today’s global building emissions. These decarbonization pathways are compared to those from global models. Results demonstrate that reference scenarios are in all countries insufficient to achieve substantial decarbonization and lead, in some regions, to significant increases, i.e., China and South America. Decarbonization scenarios lead to substantial carbon reductions within the range projected in the 2 °C scenario but are still insufficient to achieve the decarbonization goals under the 1.5 °C scenario. Building decarbonization has an important role to play in achieving global emissions reductions targets. Here the authors find that stated policy scenarios are insufficient to achieve building decarbonization goals globally, while ambitious decarbonization scenarios are still not sufficient to achieve goals under the 1.5 °C scenario.

To achieve climate neutrality by 2050, anthropogenic greenhouse gas (GHG) emissions must be reduced to net-zero. Due to its high contribution to global GHG emissions, addressing the built environment is a critical step to reaching this target. This study aims to determine the extent to which GHG emissions from the European Union’s (EU) building stock can be reduced by 2050 and which carbon removal measures are required to achieve net-zero whole life cycle emissions. Two future scenarios are assessed via bottom-up, life cycle assessment-based building stock modelling to project GHG emissions of buildings in the EU from 2020 to 2050. The first scenario represents a business-as-usual approach, while the second includes additional GHG emission reduction strategies targeting material production, selection, recycling, energy efficiency, as well as other supply- and demand-side changes. Carbon dioxide removal technologies are investigated to offset the residual GHG emissions. Results of this measure-driven modelling show that the life cycle GHG emissions of the EU building stock have the potential to be reduced by up to 72% by 2050, with residual GHG emissions of around 229 MtCO2eq in 2050. To completely offset residual emissions with currently available technologies, all new building constructions would have to be equipped with additional direct air capture technologies, starting from 2030. Feasibility concerns regarding this technology emphasize the importance of stronger action on initial decarbonization measures.

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In the context of current energy consumption and CO2 emissions, the impact of buildings is highly significant. In the quest for sustainable building management, the concept of digital twins emerges as a transformative technology, offering unprecedented capabilities for enhancing energy efficiency and reducing carbon footprints. This article explores the application of digital twins in the built environment, emphasizing their role in achieving significant energy and environmental benefits. Beginning with a brief discussion introducing the concept of digital twins, the article highlights their critical importance in modern building management and their potential to revolutionize sustainability practices. It then examines the role of IoT and AI in enabling advanced aspects of digital twin technology, including its technological functionalities. A comparative analysis of different simulation tools and methodologies follows, providing a comprehensive overview of how digital twins can be utilized to optimize building performance.

The lack of standard practices and platforms for assessing refurbishment strategies towards Circular Economy (CE) and their impact in global warming constitutes a challenge for the decarbonization of existing building stock. Incorporating data and feedback from designers and practitioners since early design stages is important to feed a multi-criteria dynamic process with multiple dimensions, which must be assessed under a life cycle perspective. To tackle this issue, this paper introduces a new methodology to support the implementation of tailored refurbishment strategies for increased recovery, reuse and recycling of construction materials. The final objective is to build a methodological framework for sustainable refurbishment design in a BIM environment, which aims to facilitate standardized practices in the construction sector, regarding CE, with a positive impact in the mitigation of global warming and the decarbonization of the building stock. To test the development of this methodology, a case study building in Lisbon, corresponding to a 1919 - 1945 archetype is analysed, making use of its BIM model, where BIM standardization criteria and circularity indicators are discussed, in order to be implemented as a Plugin for Circularity.

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In the summer of 2021, the European Commission presented a package of climate legislation (Fit for 55). It adjusts the European Union’s (EU) policies on climate, energy, land use, transport, and taxation to reduce net greenhouse gas emissions by at least 55 % by 2030 compared to 1990 levels. 2030 has become a milestone and an intermediate target for carbon neutrality in 2050. What does this mean for different energy sectors? How will energy stakeholders act to achieve the ambitious goals of climate neutrality in the coming decades? Are the goals set achievable for Latvia and Baltics, and how can building sector energy consumers help with their behavior changing and recent technology introduction? To answer these questions different building stock energy modelling scenarios have been elaborated using the Baltic Backbone model to find the best cost-effective way to meet all requirements and needs. The main results of modelling are presented and analyzed in this paper.

Given the ambitious targets for greenhouse gas emissions reduction set by the European Union and the importance of cities in achieving these goals, there is an increasing need to analyze a city’s environmental footprint with a life cycle approach. The life cycle assessment is considered the leading methodology for environmental metrics, permitting a holistic environmental perspective on cities. Life Cycle Assessment software applications are aimed at single product evaluation, making urban scale, data management, and environmental assessment complicated or impractical, mainly due to the massive data processing required. The novelty of this work is a new tool, utilizing a data-driven approach, that allows an extensive environmental evaluation of buildings (following the EN 15978 standard, considering 20 impact categories). The tool was applied to analyze the city of Milan. Approximately 240 000 building units were investigated and compared using as activity data the information described in the energy performance certificates of building units. The results for Residential, Commercial, and Retail building units (old and new) are 58, 65, and 84 kg CO2eq / (m2 of useful floor area * year), respectively, considering space heating, domestic hot water, and controlled mechanical ventilation. Actions linked with: (i) improving energy systems, (ii) the decarbonization of energy carriers (i.e., electricity and natural gas), and (iii) the retrofitting of envelopes by running the tool developed. The outcomes obtained were used to verify pieces of legislation listed in the “Fit for 55” and “REPowerEU.” The scenarios lead to the conclusion (for Milan) that the reduction of greenhouse gas emissions by at least 55% (by 2030) is achievable only by retrofitting at a rate of 1.6% per year both energy systems and envelopes, plus also acting on the decarbonization of energy carriers.

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University campuses present a unique opportunity for decarbonisation through intelligence integration for smart energy campuses. So far, the evidence-base for smart energy campuses focuses on building-level demonstrations or archetypal approaches and the university campus stock lacks a common assessment framework to characterise and evaluate smart-energy transition pathways. This paper presents a methodological framework that leverages automated computational methods (3DStock, SimStock) to produce building-by-building dynamic thermal models. The modelling method can benefit the evaluation of smart-energy campus and decarbonisation strategies and also simulate the dynamics of complex HVAC systems under demand-response where data availability is more granular. Instead of using archetypal approaches to represent the heterogeneity of building stocks, this work developed an automated building-by-building stock modelling approach based on a case study. HVAC systems are also modelled based on information from Display Energy Certificates. Model calibration is performed at stock level against actual data from Building Monitoring Systems and operational energy performance data following the CIBSE TM63 protocol. Geometry checks showed that 63% of the models matched actual geometry sufficiently, whereas energy use intensity was overestimated by around 35% across the campus in the baseline partially calibrated building models. For a typology, initial comparisons with a fully calibrated model signified lighting, cooling and heating setpoints as potential factors. A major advantage of the method is that it can be flexibly used depending on the data granularity available and, therefore, eliminates a significant barrier that Urban Energy Modelling presents in terms of data availability.