We present overhang_surrogates , a lightweight Python package that streamlines surrogate-model workflows for building-energy studies. It provides space-filling Monte Carlo sampling utilities including a Python reimplementation of the MIPT sampler with incremental extension, helpers to build batched building energy model samples and run EnergyPlus simulations, a simple interface for k -fold cross-validated XGBoost ensembles and grid predictions, and a vedo-based 3D plotting helper. By automating sampling, batched simulation, ensemble training, prediction and visualization, the package shortens time-to-prototype and lowers the barrier to reproduce and extend simulation driven surrogate experiments. The software is open-source and designed for easy reuse and extension. • Lightweight Python package for surrogate workflows in building energy. • Implements MIPT space-filling sampling with incremental extension. • Batch EnergyPlus sampling and simulation helper for rapid prototyping. • Cross-validated XGBoost ensembles and grid prediction interface. • Vedo-based helper to make publication-quality 3D diagrams easily.

Extreme cold snaps pose significant risks to buildings, infrastructure, energy systems, and occupants, yet standardized climatic datasets tailored for resilience-focused building performance modeling remain limited. This study presents a methodology and corresponding dataset of cold snap events for 217 U.S. cities, derived from 24 years of historical hourly temperature data obtained from the NASA POWER project. Cold snaps were detected using a percentile-based, location-specific threshold that identifies periods of "abnormal cold" with additional constraints to ensure that events reflect meaningful differences from local winter conditions. Each event was characterized using a suite of metrics, including event duration, heating degree hours, and overcooling degree. Events were further classified into four categories based on the mean outdoor air dry-bulb temperature, analogous to intensity scales used in other hazard domains. A selection procedure was applied to ensure that each city is represented by a small set of short, medium, and long-duration events, resulting in a curated dataset of 880 cold snaps suitable for building energy simulations and resilience assessments. The dataset is provided as EnergyPlus Weather (EPW) files accompanied by a summary spreadsheet containing all events and their metrics. This dataset supports the systematic evaluation of building performance under extreme cold weather conditions and provides a foundation for thermal and energy resilience modeling across the U.S. climates.

Building energy simulations have traditionally relied on historical weather files that do not account for climate change. This study presents an open-source, automated Python workflow to generate future hourly weather files compatible with EnergyPlus for building energy and renewable energy simulations across North America through 2100. The methodology combines quality-controlled historical data (CWEEDS, GHCNh, NSRDB) with bias-corrected NA-CORDEX projections using Quantile Delta Mapping and analogue-based temporal disaggregation. A machine learning step is introduced to decompose solar radiation for EPW compliance. The pipeline produces ready-to-use CSV and EPW files, enabling direct assessment of hourly building performance and renewable energy potential under future climate scenarios. Validation indicates plausible warming (e.g. +3°C–6°C in Winnipeg under RCP8.5) and consistent photovoltaic simulation results. The open-source Python code is made publicly available.

From comparison to integration: Building energy simulation tool variability and the case for intelligent retrofit workflows

Precise modeling of cooling and heating systems is essential for building energy simulation, particularly for designing high-energy efficiency buildings and achieving low-carbon targets. This study evaluates how performance input conditions affect the accuracy of building energy simulation with a variable refrigerant flow (VRF) heat pump system in EnergyPlus. A validated testbed model, calibrated with field measurements from the test facility in South Korea, was used to compare three case configurations: (Case 1) default EnergyPlus VRF curves, (Case 2) default curves with manufacturer-rated coefficient of performance (COP), and (Case 3) manufacturer-derived curves with the rated COP. Results indicate that simulations using manufacturer-based curves and COP achieved the closest alignment with measured data, while default inputs produced the largest deviations. In whole-building simulations, Case 1 shows substantial error, with CV(RMSE) of 59 %, and Case 2 reduces error, with CV(RMSE) of 13 %. Case 3 presents the most accurate results, presenting CV(RMSE) of 9 %. Simulation using manufacturer-derived curves and rated COP could capture the system’s real-world temperature- and load- sensitive behaviors more effectively. The novelty of this study lies in developing and validating a manufacturer-informed curve-fitting and simulation approach that quantitatively improves EnergyPlus VRF model, including a quantified error reduction from 59 % to 9 %. These findings demonstrate the critical role of precise real-world performance data in enhancing simulation accuracy and present a practical framework for improving reliability in building energy assessments and performance-oriented HVAC system design. • Performed comparative study of VRF system reflecting manufacturer-derived curves. • Validated EnergyPlus VRF system simulation model with real-world measurements. • Reduced CV(RMSE) from 59 % to 9 % using updated COP and manufacturer-based curves. • Highlighted the importance of real-data-based curves for VRF simulation reliability.

RCBldGH: Integrating enhanced RC thermal networks to parametric modeling and design for rapid building energy simulation and optimization

Accurately modelling occupant behaviour is essential for realistic building performance simulations, yet both the temporal and spatial behaviour diversity remain poorly monitored and modelled. This study develops practical methodologies and guidelines to determine the minimum monitoring duration, the number of rooms per dwelling, and the number of rooms required to develop representative occupant behaviour models. The approach was applied to a dataset of 76 French dwellings monitored for window opening behaviour during summer. Methodological advances in modelling include the use of sine and cosine transformations for time-of-day effects and a multivariate normal distribution to model occupant behaviour spatial diversity and to keep the linear correlations between opening and closing models. Results indicate that two months of monitoring, while instrumenting all rooms is necessary to represent occupant behaviour diversity. Capturing population-level diversity requires monitoring several hundred dwellings, exceeding the scope of most existing campaigns.

Background: Energy constitutes a fundamental driver of economic growth, wealth creation, and social development, with buildings accounting for a substantial proportion of global energy consumption and consequently exerting significant environmental impacts. Aims: The study aims to identify the important factors that affect the energy efficiency in various types of buildings and to compare the energy efficiency of various types of buildings so that buildings can be classified as energy efficient, neutral or energy inefficient based on quantitative comparison. Study Design: Analytical and simulation-based comparative study of residential and institutional buildings supported by manual heat-loss calculations and building energy simulation using EQUEST software. Place and Duration of Study: Department of Civil Engineering (Structural Engineering), College of Technology and Engineering, Maharana Pratap University of Agriculture & Technology (MPUAT), Udaipur, Rajasthan, India, between 2024 and 2026. Methodology: Three primary buildings were selected (1300 ft² single-storey, 1200 ft² three-storey and a 44500 ft² E-shaped institutional building) along with a 100 ft² square reference unit. Annual electricity consumption and demand were simulated using EQUEST freeware building energy analysis program considering parameters such as floor area, number of floors, shape of floor and direction of windows. Comparative manual heat-loss calculations (q = UAΔT) were performed for buildings of varying size (10'×20', 20'×50', 40'×50', 50'×50') and varying shape (circle, square, rectangle, parallelogram, triangle). For each major design parameter, the percentage contribution to overall energy efficiency was estimated. Results: EQUEST software simulations yielded efficiencies of 35.77%, 34.46%, 55.5% and 74.05% for the 1350 ft² single-storey, 1200 ft² three-storey, 44500 ft² E-shaped and 100 ft² square buildings respectively. As area increases, efficiency decreases because energy consumption increases while demand remains relatively stable. The 10'×20' standard building was 4 times, 9 times and 11.5 times more efficient than the 20'×50', 40'×50' and 50'×50' buildings respectively. Among shapes, circular plans exhibited 214% higher efficiency than square standard. Rectangle with minimum dimension 10' showed 100% efficiency. Triangle and small rectangles (10'×5') showed negative efficiency (−50%). Window orientation (north-east) contributed 1.46% efficiency gain. Sealed windows/doors contributed 1.75-fold efficiency, solar water heaters 1.81-fold, building insulation 1.84-fold, star-rated appliances 25-fold, fixture upgrades 1.80-fold, ventilation 1.95-fold, smart technology 1.70-fold, cool roofs 1.85-fold, eco-friendly materials 1.77-fold, landscaping 1.75-fold and alternative energy 1.71-fold. Conclusion: Building size and shape are the most weighted factors for energy efficiency without which a building cannot be efficient. Heat loss is the major factor of energy efficiency and temperature is a salient factor of heat loss. Orientation, insulation and ventilation have major roles in efficiency. Buildings with positive efficiency are energy efficient and good for occupant health. Installation of energy-efficient systems contributes to sustainable development goals and aligns with India's commitment to reduce greenhouse gas emissions by 35% by 2030.

Nonwoven polymer fibrous materials have been widely adopted in passive radiative cooling due to their controllable subwavelength dimensions that relate to the visible to mid-infrared light regulation. However, existing radiative cooling fabrics, whether electrospun 2D nonwovens made of straight nanofibers or multilayer fabrics with functional coatings, sacrifice the crucial textile function of air-moisture permeability. Here, we explore the manufacturing and optical property engineering of 3D helical nanofibers for efficient radiative cooling alongside highly permeable air purification. Inspired by biological tendrils, cellulose acetate (CA) and polyvinylidene fluoride-hexafluoropropylene (PVDF-HFP) are cospun via air-blown electrospinning, where their mechanical properties mismatch combined with electrostatic airflow perturbation drive the formation of helical fibers. The resulting helical nanofiber metafabric (HNMF) with its hierarchically porous nanohelix architecture and CA/PVDF-HFP molecular backbones, achieves ∼96% solar reflectance and ∼91% emission within the atmospheric transmission window. This enables effective radiative cooling while simultaneously removing >99.9% of PM0.3 particles at a low-pressure drop (52.8 Pa). Outdoor evaluations and building energy simulation confirm the practical applicability of HNMF for use as protective curtain and masks in addressing environmental thermal stress and particulate matter exposure, highlighting its potential as a multifunctional and scalable material platform for wearable personal protection and energy-efficient building solutions.

Integrating solar energy systems into high-rise multi-unit residential buildings (MURBs) in urban environments involves complex interactions between façade design, urban shading, and economic constraints. This study develops a simulation-optimization framework to evaluate solar energy collection in a high-rise MURB in a dense urban context. The framework integrates building performance simulations with a multi-objective evolutionary algorithm (NSGA-II) to optimize façade and rooftop photovoltaic (PV) systems with window configurations. Design variables include PV type, rooftop PV tilt angle, façade PV widths and heights, and window thermal and geometric properties to minimize net energy consumption (NEC) of the building and life cycle cost (LCC), while maintaining thermal comfort. The framework is tested on a case study to evaluate the workflow. Optimal solutions consistently minimize window widths, which increase façade area available for PV and reduce building energy use, while urban shading affects the optimal vertical placement of PV, with the south façade supporting the largest coverage. Pareto results reveal that reducing NEC below a threshold sharply raises costs, underscoring the tradeoff between energy efficiency and investment feasibility. These findings demonstrate the value of integrating real design constraints and HVAC characteristics into an optimization framework to develop cost-aware, feasible strategies for cold-climate residential towers.

• GA tool optimizes envelope, HVAC, DHW, PV, and batteries together. • Moderate envelope upgrades are cost efficient. • Heat pumps with hybrid PV battery outperform gas systems. • High-star upgrades existing stock do not achieve cost-optimality under assumed conditions. • Tool scales from single dwellings to portfolio studies. Existing residential retrofit tools typically optimise a narrow set of measures, use simplified economic assumptions, or are not directly compatible with industry-standard energy rating software, limiting their usefulness for practitioners and policymakers. This paper presents a Lifetime Energy Cost Model (LECM) that couples AccuRate building energy simulations with a tariff-based energy cost model and uses a genetic algorithm to identify least-cost energy efficiency retrofit configurations for existing dwellings. The model minimises 15-year annualised cost under current and projected energy tariffs, retrofit installation costs and rebates. The framework simultaneously explores building envelope retrofits (floor, wall and ceiling insulation, and windows), electrification of HVAC and water heating appliances, rooftop solar and battery storage. The LECM is applied to a representative pre-2000 detached dwelling in Melbourne. Validation against a brute-force search for restricted design spaces confirms that the genetic algorithm reliably identifies the global minimum, demonstrating robust convergence and computational efficiency. For the case-study dwelling, the least-cost configurations prioritise cost-effective envelope upgrades involving R2.0 ceiling insulation, R2.0 floor insulation, R1.9 blow-in wall insulation and improved seals for airtightness (raising the NatHERS energy rating to around 5 stars), replacement of gas appliances with heat pump HVAC and hot water systems, and the installation of a rooftop solar system of around 4.0 kW. Together, these measures reduce energy bills by approximately 75% with a payback period of about 9 years. Larger export-oriented solar systems and battery storage are only financially attractive under specific rebate and tariff conditions. Sensitivity analysis indicates that capital cost and financing assumptions are the dominant drivers of economic viability, with energy tariff forecasts exerting a secondary influence. The results illustrate how an integrated, cost-focused optimisation framework can support the design of practical, least-cost decarbonisation pathways for existing housing in temperate climates.

With global warming and accelerated urbanization, building air-conditioning (AC) releases more heat into the environment, exacerbating the urban heat island (UHI) effects and increasing building cooling energy consumption. Existing research has limited quantification of the impact of air-conditioning anthropogenic heat (ACAH) on the cooling energy consumption of different types. This study aims to explore the distribution characteristics of ACAH and its impact on residential building energy consumption. Firstly, typical residential buildings in the Pearl River Delta region were selected as a case study. Field experiments were conducted to measure temperature and humidity at 0.5 m, 1 m, 2 m, and 3 m from the outdoor unit, alongside ambient temperature and wind speed. Three grid densities were applied to verify the CFD model, with a prediction error of less than 0.3 °C at 0.5 m under a medium grid. The simulated temperature at 1 m from the outdoor unit under calm wind conditions was compared with field measurements to reveal the horizontal and vertical distribution characteristics of ACAH. Secondly, the effects of different building shapes, ambient temperatures, and wind speeds on the spatial distribution of ACAH were investigated. Finally, EnergyPlus (V23.1.0) was employed as the building energy simulation software, with its microclimate coupling interface implemented via Python scripts to quantify cooling energy consumption variations across different building floors under ACAH influence. Results indicated that ACAH exhibits significant horizontal non-uniformity, exerting the greatest impact within a 0.5 m radius (affected air temperature 4.3 °C higher than ambient). Vertically, localized heat accumulation occurs in the building’s central area, with air temperature 3.5 °C higher than at the bottom. Furthermore, compared to fixed meteorological conditions, the cooling energy consumption difference across floors considering ACAH reaches approximately 7.8%. This study provides accurate meteorological boundary conditions for building energy assessment and supports microclimate management in residential areas.

Natural ventilation is widely regarded as an energy-saving strategy in buildings; however, under continuous mechanical ventilation in Japanese residential buildings, its performance remains insufficiently understood. This study evaluates the performance of different natural ventilation strategies for a typical two-story detached house across eight climate zones in Japan using dynamic building energy simulation. Four ventilation strategies are examined, including baseline mechanical ventilation (S0), shoulder-season natural ventilation (S1), summer night ventilation (S2), and an adaptive natural ventilation strategy with humidity constraints (S3). Annual HVAC loads, monthly variations, and the structure of cooling loads are analyzed. Results show that shoulder-season natural ventilation (S1) does not lead to energy savings and may result in a slight increase in annual HVAC loads in most climate zones. In contrast, summer night ventilation (S2) reduces annual HVAC loads by approximately 8–10% in transitional climates (CZ3–CZ5), while its effect is weaker in hot and humid regions. The adaptive strategy (S3) achieves moderate reductions of up to about 2–3% and significantly decreases the proportion of latent cooling loads. Overall, the effectiveness of natural ventilation is governed by the trade-off between sensible load reduction and latent load increase and is strongly climate-dependent. These findings provide a basis for optimizing hybrid ventilation strategies under continuous mechanical ventilation conditions.

Effective architectural design requires integrating multiple attributes to achieve optimal outcomes, yet students often struggle to balance these technical and conceptual demands. This study assesses the design preferences of undergraduate architecture students at Bingham University Karu to understand how these priorities impact their design outcomes and professional readiness. A quantitative research methodology was employed, utilizing a structured online questionnaire distributed to 189 students from the 200 to 400 levels. A total of 111 responses (58.7% response rate) were analyzed using descriptive statistics to determine mean scores and preference rankings across four key attributes: Interior Space Planning, Building Form and Appearance, Sustainability, and Site Planning. The findings revealed that students significantly prioritized Interior Space Planning, which achieved the highest mean score (4.44) and frequency of preference. Sustainability and Site Planning followed, while Building Form and Appearance received the least attention, ranking last with a mean score of 3.79. Data indicated that while students acknowledge site integration as important, it is often viewed as a supervisory requirement rather than a personal design passion. The results suggest a shift in architectural education away from pure formalism toward a functionalist, performance-based approach. However, the disparity in attribute prioritization highlights a gap in achieving a comprehensive skill set required for professional practice. The study recommends that architectural educators emphasize the integration of diverse attributes, particularly site responsiveness and incorporate building performance simulation tools to bridge the gap between functional necessity and technical validation

This paper presents a comprehensive framework for the context of integrating urban digital twins with the high-performance computing (HPC) to model, analyse, as well as accelerate pathways to city-wide carbon neutrality. Urban abstract simulations consist of digital twins of cities integrating heterogenous streams of data, physical models, and machine learning surrogates to simulate the behaviour and conditions of cities both spatially and temporally. HPC provides computational envelope capable of executing detailed building energy modelling, transport systems modelling, renewable generation modelling and coupled atmosphere-urban microclimate modelling both at city scale and at a temporal scale suitable to do sound policy analysis. The modular digital twin system comprises a system of interaction between building energy simulation and urban mobility models and distributed energy resource (DER) models and an urban carbon scoring engine. Its structure experiences HPC in order to compute a great number of scenarios in order to measure uncertainty and apply multi-objective analysis with the aim of minimizing emissions, costs and resilience. To explain the approach, a case study of synthetic city is conducted, and sensitivity experiments of electrification, deep retrofit, distributed photovoltaics, storage, and demand response portfolios is carried. Results quantify trade-offs between decarbonization rate, cost of energy, and stressors resilience and show that HPC-based digital twin’s communities can be used to discover near-Paretooptimal trade-offs in the presence of epistemic and aleatory uncertainty. The last point is on the implementation problems, data handlings and research plans to operationalize the city digital twins as a decision support system to simplify into carbon neutral city.

Thermochemical energy storage (TCES) using salt hydrates offers high energy density and long-duration storage potential for building applications. This study presents a reduced-order, axially discretized model of a closed-system SrBr2·6H2O reactor that is designed for integration into transient building energy simulations. The model captures both hydration and dehydration processes and was used to inform the design of two experimental reactors: a single tube-in-tube configuration and a multi-tube shell-and-tube configuration. The facility used to test these reactors consists of a glycol loop for managing heat and an evapocondenser for managing water vapor flows. Experimental results reveal key differences in heat and mass transfer behavior between the two reactor designs. The single tube-in-tube reactor exhibited heat transfer limitations that aligned with model predictions, while the multi-tube reactor demonstrated mass transfer constraints that were not fully captured by the model. Discrepancies between measured and simulated performance highlight the need for improved characterization of reaction kinetics, thermal contact resistance, and vapor transport. This work advances the understanding of salt hydrate TCES systems and provides a validated modeling framework that supports future integration into whole-building energy analyses.

Assessing the energy performance of integrated living wall systems: a dynamic numerical model coupled with TRNSYS for building energy simulations

Urban microclimate directly affects building energy demand, particularly for the cooling and heating energy requirements. In this study, taking Harbin as an example, the local climate zone (LCZ) scheme, fixed-point weather station monitoring and EnergyPlus-based building energy simulation were employed to investigate the influence of LCZ type on building cooling and heating loads. Correction factors for Typical Meteorological Year (TMY)-based predictions were evaluated. The results show that urban microclimates could exert a more pronounced impact on cooling loads. Compared with the TMY-based prediction results, the annual cumulative heating load intensity (ACHLI) of a building under different LCZs was decreased by 5.0%–17.9%, the annual cumulative cooling load intensity (ACCLI) was increased by 12.3%–27.8%, and the total load was decreased by 0.1%–11.6%. The ACHLI and annual peak heating load intensity (APHLI) of the built types were generally lower than those of the land cover types, with an average reduction of 6.0% and 3.0%, respectively. Compared with TMY-based prediction, the correction factors of the ACHLI, ACCLI, APHLI and APCLI ranged from 0.82–0.97, 1.09–1.35, 0.97–1.14 and 1.02–1.16, respectively. These results have provided a theoretical framework for accurate load prediction, enhancing building energy standards and climate-responsive planning in severe cold regions.

Office workers respond to a variety of environmental and contextual stimuli, which can influence their comfort and behaviour. Although recent models address the unpredictability of human–building interactions, they often neglect multi-domain factors and user motivations. A holistic understanding entails environmental, personal and contextual influences. The use of direct occupant feedback can reveal perceptions and drivers of behaviour that physical data alone cannot capture. This study introduces a multi-domain and mixed-method data collection framework for open-plan offices entitled eCOMBINE. This framework integrates both subjective and objective data across domains to better capture the factors influencing occupant behaviour and perception, particularly in buildings with operable windows and blinds. Central to this framework is the ‘OBdrive’ mobile application, which records self-reported motivations for window, blind and light control actions. The framework was implemented in two Swiss case studies, involving 44 participants. Results demonstrate the framework’s feasibility and participant acceptance. Benefits arise from embedding comprehensive data collection within research workflows. The eCOMBINE framework can support future research and inform more effective and efficient building design and operation by investigating user motivations, adaptive actions, health impacts, behaviour-driven simulations and optimal sensor set-ups. PRACTICE RELEVANCE Open-plan offices merit specific attention in energy-related behavioural research as their occupants typically have diverse personal needs and preferences that need to be met in a shared environment. The eCOMBINE framework adopts an integrated approach to study the relationships among combined indoor environmental quality, occupant behaviour, global environmental comfort and building energy use. By capturing multi-domain environmental conditions alongside subjective perceptions, motivations and contextual variables, eCOMBINE enables research into human–building interactions and facilitates integrated analysis into post-occupancy evaluation. These insights can support the development of more accurate and human-centred behavioural models, which in turn can help to improve the predictive reliability of building performance simulations. Such models can be used to optimise office building design and enhance both indoor environmental quality and energy performance by considering the synergies and trade-offs across environmental domains, adaptive opportunities, individual preferences and workplace context.

In recent years, balcony plug-and-play photovoltaic (PV) systems have experienced significant growth in Europe, driven by rising electricity prices, environmental awareness, and ease of installation. These modular systems can be directly connected to a household socket without professional installation, making them ideal for apartment residents lacking rooftop access. Their investment cost per kilowatt is nearly half that of conventional rooftop PV, lowering the entry barrier for urban households seeking to reduce energy bills and carbon emissions. This study analyzes the performance of balcony plug-and-play PV systems for Istanbul, Türkiye. Using BEopt building energy simulation software, typical low-, medium-, and high-electricity consumption apartments were modeled to estimate annual electricity demand under Istanbul’s climatic conditions. PV performance was evaluated for different façade orientations (south, east–west, and north) and module tilt angles (70°, 80°, and 90°). Results show that south-facing, 70° tilted systems consistently achieved the highest energy yields and economic performance. For low-consumption households, the 500 W system achieved the shortest payback period of 6.7 years, whereas 1 kW systems performed best for medium- and high-consumption households, with payback periods of 6.3 and 5.7 years, respectively. East–west orientations also performed well, while north-facing systems were least economically viable. These findings suggest that balcony plug-and-play PV systems could offer a practical and cost-effective solution to Türkiye’s low residential PV penetration, enabling widespread adoption in multi-story apartments without complex installation or property issues.