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Model predictive control enhances building energy performance; however, its reliability is highly dependent on the robustness of internal prediction models under severe operating conditions. To address this, a risk-aware model-then-control (RAMC) training framework is proposed in this study. This approach augments conventional prediction loss with a conditional value-at-risk (CVaR) penalty on operational costs under perturbed inputs, embedding tail-risk awareness directly into the prediction model. The framework is trained via standard backpropagation, avoiding the computational burden of differentiating through the controller. The proposed methodology is evaluated on a simulated commercial building equipped with a hydronic heating system under three weather scenarios. Compared to a standard fidelity-trained baseline, the strongest risk-aware configuration reduced occupied cold degree-hours by 22–26% and peak cold violations by 14–27%, demonstrating the greatest benefit under forecast bias. These comfort improvements were achieved alongside a 17–31% increase in weekly heating energy consumption. The results indicate that embedding tail-risk awareness into model training improves closed-loop comfort robustness relative to standard accuracy-based training. An ablation study attributes this improvement directly to the CVaR tail term, while the risk weight formalizes a tunable energy–comfort trade-off dictated by operational priorities. revtwogreenIn this case study, a fixed setpoint-margin baseline reached comparable cold protection at lower energy; the distinct contribution of RAMC is that it relocates a tunable tail-risk preference into the prediction model itself, leaving the downstream controller unchanged. Full article

Raising indoor temperature setpoints is widely promoted as a practical way to reduce cooling-related energy demand in commercial buildings, yet its net carbon impact becomes uncertain once behavioral rebound effects are considered. This study develops an integrated carbon-accounting framework to evaluate the climate implications of summer indoor temperature increases of 1–3 °C in U.S. mercantile buildings. The framework combines operational energy savings from reduced cooling demand with consumption-driven emissions arising from longer customer dwell times and increased consumer spending under improved thermal comfort conditions. Carbon outcomes are quantified using sector-level electricity data and the USEEIO emission factor for retail trade. The results reveal a clear imbalance: operational carbon savings range from 0.21 to 0.64 Mt CO₂, whereas consumption-driven emissions range from 3.37 to 21.90 Mt CO₂, yielding a consistently positive net carbon impact of 3.16–21.26 Mt CO₂ across all scenarios. A break-even analysis indicates that only 1.30–3.89 billion USD in additional spending is sufficient to offset the operational savings. The findings remained robust across alternative behavioral and carbon-accounting specifications; a 10,000-iteration Monte Carlo analysis produced positive net carbon impacts in every simulation (median 8.54 Mt CO₂; P(NCI > 0) = 1.00). Overall, the results suggest that temperature-based efficiency measures may overstate their climate benefits when behavioral responses are ignored, highlighting the importance of incorporating rebound effects into building energy assessments and commercial climate policy. Full article

Driven by the demand for high-quality housing, fourth-generation residential buildings—known internationally as “Vertical Forest” and in China as “Urban Forest Garden”—have developed rapidly. Initially built in mild southern regions, they have recently expanded to colder northern areas, with over 50 projects underway in provinces such as Shanxi, Hebei, Shaanxi, and Gansu. Several cities have introduced design standards and incentives, and the China Association for Standardization of Engineering Construction has issued the “Design Standards for Urban Forest Garden Housing.” However, in cold regions, where winters are long and cold and summers are short and hot, there is a lack of systematic quantitative research on how balcony design affects building carbon reduction, energy efficiency, and indoor thermal comfort. To address this research gap, this paper poses the following research questions: (1) In fourth-generation residential buildings in cold regions, how do different combinations of balcony orientations affect annual energy consumption and indoor thermal comfort? (2) Which balcony configurations offer the best balance between carbon reduction, energy efficiency, and thermal comfort? Based on statistical analysis of terrace configurations from more than 40 projects, 12 typical configuration models were identified. Using Ladybug and Honeybee tools on the Grasshopper platform, building energy consumption and indoor thermal comfort were simulated. Multi-objective trade-off analysis was performed using the Pareto front method. In this study, indoor thermal comfort was evaluated using the PMV (Predicted Mean Vote) index. PMV is an index proposed by Professor Fanger that comprehensively reflects human thermal sensation, taking into account air temperature, humidity, wind speed, mean radiant temperature, human metabolic rate, and clothing thermal resistance. Its typical range is −3 (cold) to +3 (hot); in this study, the comfort zone was defined as −1 ≤ PMV ≤ 1. Key findings: (1) The southwest + south terrace configuration shows the highest annual energy consumption, exceeding the lowest (northwest + west) by 2.7%, indicating that south-facing terraces are less favorable for carbon reduction. (2) The best thermal comfort is achieved with east, west, and south orientations. Compared to the least comfortable combination (southwest + northwest), the difference in PMV comfort percentage reaches 2.4%. (3) The Pareto front reveals that beyond a certain comfort level, energy consumption increases sharply. The west + south and east + south combinations yield the highest thermal comfort (49.4%) while maintaining relatively low energy consumption (17.98 kWh/m²). Therefore, in cold regions, fourth-generation residential designs should prioritize terrace combinations integrating south-facing and side-facing orientations and avoid pure corner configurations to balance winter solar gain and summer shading. Full article

Public lighting can account for nearly 40% of municipal energy consumption in some European cities and plays a vital role in road safety, mobility, and the quality of public spaces. Despite notable efficiency gains from the widespread adoption of light-emitting diode (LED) technologies, the technical outputs of standards-based and installation-level assessment methods are not usually simple and communicable energy-performance labels for municipal decision-making. This study addresses this issue by introducing an algorithm-based framework for classifying energy performance in public lighting at the road-segment level. This approach translates existing lighting standards and efficiency indicators into a straightforward and understandable energy label, adapting the energy labelling concept, commonly used for buildings and appliances, to public space infrastructure. This framework is implemented through a national digital platform for public lighting classification, which has already attracted formal interest from more than 100 municipalities, indicating strong institutional uptake. The results indicate that road-segment-level energy classification is feasible and scalable as a voluntary tool to enhance municipal accountability and support informed decision-making. This study concludes that algorithmic energy labels for public lighting can support sustainable urban governance transparency, comparability and decision-making capacity, with future research aimed at building capacity for large-scale implementation and incorporating environmental, human health, and ecological impact considerations into the classification system. Full article

This study presents a BIM-integrated life cycle analysis framework (screening-level) for climate-responsive urban energy performance assessment at district scale. The methodology addresses the need for consistent evaluation of operational energy demand under both design interventions and future climate conditions. A mixed-use district in Milan is used as a case study, where parametric BIM massing models (LOD 200–300) are coupled with building energy simulation to analyze three scenarios: a baseline configuration (S0), an envelope optimization scenario (S1), and a future climate scenario based on CMIP6 morphed weather data (S2). The framework enables comparative assessment of energy performance across consistent geometric, operational, and climatic assumptions. Results indicate that envelope optimization reduces energy use intensity by approximately 15–22% across building typologies. Under future climate conditions, cooling demand increases significantly, while reduced heating requirements result in a total district energy use intensity of 33.6 kWh/m²·year (1.60 GWh/year). An indicative carbon assessment based on simulated energy use highlights cooling-driven electricity as the dominant contributor to operational emissions under future conditions. The findings demonstrate that climate change primarily redistributes energy demand between heating and cooling rather than uniformly increasing total consumption, and confirm the value of BIM-integrated, scenario-based workflows for supporting climate-responsive urban design decisions. Full article

Building energy consumption accounts for a significant proportion of total societal energy consumption, and reducing building energy consumption is critical to the global mission of reducing emissions. Windows are regarded as the least energy-efficient component of a building’s envelope. This study examines service-area buildings fitted with high-performance glass in Chinese cities across various climates and employs the non-dominated sorting genetic algorithm II (NSGA-II) genetic algorithm for multi-objective optimization. In considering design variables such as building orientation and wall insulation, advanced passive design strategies, including electrochromic and aerogel glass, are incorporated into the optimization process to minimize construction costs and operational carbon emissions. Sensitivity analyses were conducted to evaluate the impact of each design variable on building operational carbon emissions. The optimal solution within the Pareto optimal set was further evaluated using the technique for order preference by similarity to ideal solution (TOPSIS) decision-making method, and the preferred energy-saving solution was quantitatively analyzed. The results indicate that optimization leads to a reduction of approximately 7.70–10.50% in annual operational carbon emissions for service-area buildings across different regions, compared to the base case, with a payback period ranging from 4.90 to 13.56 years. The proposed method contributes to sustainable building design by jointly quantifying carbon-emission reduction, construction cost, and payback period, thereby supporting climate-responsive and economically feasible low-carbon envelope decisions for service-area buildings. Full article

The transition toward next-generation energy systems requires advanced computational tools capable of supporting accurate, adaptive, and data-driven energy performance assessment. Within this context, Energy Performance Indicators (EnPIs) established under the ISO 50001 framework remain essential for monitoring energy efficiency and continuous improvement; however, conventional indicators are often based on static or simplified relationships that do not adequately capture the dynamic, nonlinear, and multivariable behavior of modern buildings and energy management systems. This systematic review analyzes the integration of ISO 50001-based EnPIs with intelligent algorithms and artificial intelligence techniques for enhanced energy management. The review follows a PRISMA-inspired methodology, using Scopus as the primary database and Web of Science and Google Scholar as complementary sources. From 5442 initial records, 2691 studies were screened and 283 articles were selected for detailed analysis, supported by a bibliometric keyword co-occurrence analysis using VOSviewer 1.6.20. The results show a clear evolution from traditional energy indicators and normalized baselines toward computational modeling approaches based on regression analysis, machine learning, deep learning, forecasting, anomaly detection, and optimization algorithms. These methods improve the predictive capability, adaptability, and operational relevance of EnPIs by incorporating climatic, occupancy, temporal, and operational variables. The reviewed evidence indicates that intelligent algorithms can strengthen ISO 50001 energy management systems by enabling dynamic baselines, early detection of abnormal consumption patterns, predictive decision-making, and continuous operational optimization. Nevertheless, challenges remain regarding data quality, model interpretability, methodological standardization, and practical integration into certified energy management frameworks. Overall, this review highlights that the future of energy performance assessment does not rely on replacing conventional EnPIs, but on transforming them into intelligent, computationally supported indicators for sustainable, resilient, and next-generation energy management systems. Full article

Highway service-area buildings are characterized by long operating hours, diverse functional spaces, and considerable energy consumption, resulting in significant life-cycle carbon emissions. This study quantifies life-cycle carbon emissions of the buildings in highway service areas. A life-cycle accounting framework was established, and net emissions were further evaluated by considering the contributions of photovoltaic (PV) electricity and vegetation carbon sinks. Five representative service areas covering hot-summer/cold-winter, severe-cold, cold, temperate, and hot-summer/warm-winter zones were investigated through field surveys and indoor thermal environment measurements to obtain envelope properties, equipment configurations, and operating profiles. Results revealed that life-cycle carbon emissions vary substantially across climatic regions, ranging from 4.31 × 10³ to 3.06 × 10⁴ tCO₂e. The operational stage accounts for the largest share of total emissions, approximately 61–84%. Heating demand dominates operational emissions in severe-cold and cold regions, whereas cooling and lighting loads become increasingly important in warm and temperate climates. The orthogonal analysis reveals significant differences in the sensitivity of design parameters across climatic regions. After implementing climate-adaptive optimization measures, life-cycle carbon emissions are reduced by 40.35–87.94% in four service areas. In the hot-summer/warm-winter region, the combined effects of PV electricity generation and vegetation carbon sinks maintain a net-negative carbon balance. The findings provide evidence for sustainable highway service-area design by linking life-cycle accounting, climate-specific design priorities, and renewable-energy substitution. Full article

Buildings and construction account for nearly one-third of global energy demand, making the thermal performance of building envelopes an important factor in reducing heating and cooling loads. Conventional steady-state indicators are useful for comparison, but they do not always describe how complete assemblies respond to changing outdoor conditions. This study presents a dynamic experimental method for evaluating construction assemblies using energy compensation under controlled indoor conditions. A highly insulated test module was used to maintain a stable indoor temperature while measuring the electrical heating energy required to compensate for heat losses through interchangeable assemblies exposed to outdoor temperature variations. Three systems were tested: double gypsum board, hollow concrete block, and externally insulated hollow concrete block. To compare the assemblies under different outdoor conditions, corrected heating energy was normalized by test area and heating degree-hours (HDHs), resulting in a Dynamic Thermal Performance Index (DTPI). The DTPI decreased from 52.4 Wh/(m²·°C·h) for the double gypsum board assembly to 21.5 Wh/(m²·°C·h) for the externally insulated hollow concrete block, while the mean corrected heating energy decreased from 2.23 to 0.91 kWh/day. The gypsum board assembly showed the fastest thermal response and the highest energy demand, whereas the externally insulated block provided the most stable indoor-side behavior by reducing the effect of outdoor fluctuations on the concrete mass. The method links transient thermal behavior with measured energy consumption and offers a practical complement to conventional steady-state metrics for comparing building envelope systems. Full article

With the continuous growth in global energy consumption and the increase in the proportion of energy use attributed to buildings, the development of highly efficient and energy-saving building materials has become necessary for reducing energy demands and greenhouse gas emissions. Phase change materials (PCMs) exhibit great potential for enhancing the thermal inertia of buildings owing to their ability to efficiently absorb and release latent heat during phase transitions. In this study n-hexadecane@ polymethyl methacrylate (16-MMWS-K) microcapsules (where “@” denotes the core-shell encapsulation structure) with a crosslinked structure were successfully prepared via emulsion polymerization, using n-hexadecane as the core material and polymethyl methacrylate as the shell. The prepared microcapsules were incorporated into a cement matrix to fabricate a phase-change energy-storage composite material. The morphology, structure, and thermal properties of the microcapsules, as well as their effects on the thermal and mechanical performance of the cement composites, were systematically investigated. The prepared 16-MMWS-K microcapsules exhibited a well-defined core–shell structure, excellent thermal stability, and a suitable phase-change temperature. Increasing the microcapsule content significantly enhanced the thermal energy storage capacity of the cement composites, reduced thermal conductivity, improved hydrophobicity, and demonstrated effective temperature regulation in building simulation experiments. This study provides both theoretical insight and experimental evidence supporting the practical application of 16-MMWS-K microcapsules in cement composites.The 28-day compressive strength (51.7 MPa) remains acceptable despite higher porosity and slight strength reduction. Full article

Buildings account for a substantial share of global energy consumption and greenhouse gas emissions, while a large proportion of the existing building stock remains energy inefficient. Thermal transmittance is a fundamental indicator for assessing the thermal performance of historic building envelopes. This study investigates the application of UAV-based quantitative infrared thermography (UAV-QIRT) for in situ U-value measurement as an alternative to conventional methods. This study proposes a structured workflow for UAV-QIRT-based U-value measurement, developed in accordance with BS EN ISO 6781-1:2023. The study evaluates four U-value calculation formulas using thermographic data acquired during an in situ case study and compares the resulting estimates with a reference U-value obtained using the heat flow meter (HFM) method. The results demonstrate that the reliability of UAV-QIRT-based U-value estimation strongly depends on outdoor thermal boundary conditions and the physical assumptions embedded within the heat balance model. In this case, the measured exterior wall surface temperature was lower than the outdoor air temperature, causing simplified formulas to produce physically unrealistic negative U-values. In contrast, the complete heat balance model, which accounts for radiative exchanges with the sky, surroundings, and ground, as well as convective heat transfer, generated more plausible estimates. Nevertheless, significant discrepancies were observed between the UAV-QIRT and HFM estimates. Sensitivity analysis revealed a high dependence of UAV-QIRT-derived U-values on environmental boundary conditions, including wind speed, outdoor air temperature, and exterior surface temperature. Full article

Globally, wildfires are increasingly threatening forest ecosystems and human well-being, requiring proactive management strategies. Integrating wildfire mitigation with bioenergy production presents a dual opportunity to reduce fire risk while contributing to clean energy. This study builds upon previous work by incorporating updated annual heat load estimates from 32 off-grid communities in northern Canada to assess the amount of biomass at risk of wildfire that could be mobilized to meet local bioenergy needs. Our results reveal that energy consumption in the remote communities considered was previously significantly underestimated, with an average of 11,710 MWh per year, and a minimum and maximum of 1869 and 43,867 MWh per year, respectively. With the updated dataset, which includes both space heating and electricity energy usage, the average energy demand is approximately 300% higher than earlier estimates. Despite this substantial increase in energy consumption, the amount of biomass needed to meet local energy demand per year ranges from 352 to 8276 odt per year, representing only a small fraction (approximately 1.67% on average) of the total biomass identified as being at risk within a 10 km buffer. This corresponds to fuel treatment areas ranging from 4 to 222 hectares per year (around 51 ha on average), depending on the community. The results presented here, based on updated energy data, provide important insights into the operational feasibility of this approach. To be successful, implementation will require strong community leadership and collaboration with fire management agencies to design consistent and cost-effective fuel treatment strategies that are tailored to each community’s environmental conditions and energy needs. Full article

In the context of the global transition toward sustainability and energy efficiency, the retrofitting of existing service buildings has become a strategic priority. With the increasing adoption of electric vehicles (EVs) and the need to reduce greenhouse gas emissions, adapting these buildings is essential to achieving low-carbon urban environments. This paper presents a numerical tool developed to simulate the energy performance of a service building and to evaluate the impact of multiple energy efficiency measures on energy consumption and CO₂ emissions. The assessed measures include the installation of photovoltaic panels on roofs and facades, optimization of Heating, Ventilation, and Air Conditioning (HVAC) systems through temperature set-point adjustments, improvements to the building envelope and integration of electric mobility infrastructure. The analysis focuses on an existing building in Lisbon, Portugal, considering both individual and combined effects of these strategies. The results indicate that combined implementation of all measures, including EV integration, can reduce energy demand and CO₂ emissions by up to approximately 50%. However, regulatory uncertainty regarding EV accounting remains a challenge, highlighting the need for clearer policies to support sustainable urban transformation. Full article

Under carbon peaking and neutrality goals, building integrated with photovoltaics and energy storage system clusters (BIPECs) enable efficient on-site renewable energy use and can act as dispatch units for the public grid. However, BIPECs face significant uncertainties and are still under development. This study proposes a decentralized cooperative power dispatch model coupling a multi-agent proximal policy optimization (MAPPO) algorithm and offline digital twin (ODT) technology to optimize the photovoltaic (PV) power consumption of clusters despite limited data availability. An integrated BIPEC energy system model is established, and by leveraging the multi-agent system model of the BIPEC, the decentralized dispatch problem is converted into a fully cooperative multi-agent reinforcement learning (MARL) problem. A simulation-assisted ODT framework constructs a digital environment for MAPPO to augment data, conduct MAPPO training, and optimize the reward function, thereby obtaining power dispatch strategies. The results show that the proposed optimization model can obtain dispatch strategies that reflect a high degree of collaboration, reducing the cumulative power supply from the public grid by 0.55–2.56% per month compared to the non-cooperative self-generating and self-using strategy. This study presents the application of MARL in BIPECs by introducing a decentralized collaborative power dispatch methodology for building clusters, enhancing building energy efficiency and facilitating flexible collaborative power dispatch. Full article

This study, through a hybrid approach to post-occupancy evaluation (POE) of four types of high-energy-efficiency housing in rural Scotland, explores the manifestation, formation mechanism, and mitigation pathways of energy risks in high-energy-efficiency housing from environmental and socioeconomic dimensions. The findings reveal a “high-efficiency paradox”: better fabric performance and lower heating demand do not guarantee reduced carbon emissions, fuel poverty alleviation, or energy resilience. Actual energy risks are formed by the combined effects of multiple factors, including building size, energy infrastructure, resident characteristics, energy prices, and policy, exhibiting a clear systemic coupling characteristic. The study further verifies that, in the context of rural Scotland, relying solely on indicators such as EPC may lead to misjudgements of housing sustainability. Heating demand, total energy consumption, carbon emissions, and energy expenditure exhibit a partially decoupled relationship. Thus, rural housing sustainability should shift from a technically efficient approach to a comprehensive strategy integrating design, infrastructure, affordability, and social equity. The study proposes context-specific mitigation pathways including multi-source energy systems, place-sensitive policies, socio-economic support, and a multi-criteria assessment framework, providing empirical references for rural housing energy transition and energy risk governance. Full article