Search Results (265)

Improving residential energy efficiency is essential to meeting UK net-zero targets, yet retrofit uptake in the private rented sector (PRS) remains limited. While many studies examine retrofit measures or Energy Performance Certificates (EPCs), few integrate comparative technology performance, cost–benefit outcomes, and landlord–tenant perspectives within a single housing context. This paper addresses that gap through a mixed-methods case study of a professionally managed private rented housing portfolio in South London, assessing four retrofit technologies: photovoltaic (PV) panels, air source heat pumps (ASHPs), double glazing (DG), and insulation. Quantitative analysis showed that ASHPs delivered the greatest EPC improvement, with 54.5% of properties achieving a two-band uplift, while PV panels offered the strongest financial return, with an average payback period of 11.7 years. Houses achieved the strongest overall results, with combined PV + ASHP retrofits delivering the best technical and financial performance; however, this pairing was only feasible in houses because of the physical requirements for both roof space and external unit installation, whereas flats and maisonettes were more constrained by space and installation feasibility. Stakeholder analysis findings revealed knowledge and incentive gaps: many tenants overestimated the effectiveness of double glazing, while landlords identified high upfront costs and delivery challenges as key barriers. Wider PRS decarbonisation will therefore require stronger policy support, streamlined retrofit delivery, and improved tenant awareness. Full article

Solar chimneys represent an effective passive ventilation technology capable of improving indoor thermal comfort while reducing building energy consumption. In this study, the thermal and fluid dynamic performance of a solar chimney integrated into a residential building located in Bordj Bou Arréridj (Eastern Algeria) was investigated through a comprehensive numerical, predictive, and optimization framework. A transient mathematical model was developed to evaluate the influence of key geometric parameters, including chimney width and inlet opening width, as well as environmental factors such as solar radiation intensity and wind speed, on the system performance. The generated simulation database was subsequently employed to develop and compare four machine learning models, namely, Artificial Neural Networks with Bayesian Regularization (ANN-BR), Deep Neural Networks optimized by Improved Grey Wolf Optimization (DNN-IGWO), k-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGBoost), for predicting eight output parameters including glazing temperature, fluid temperature, absorber temperature, outlet temperature, thermal efficiency, air change rate (ACH), mass flow rate, and outlet velocity. The results demonstrated that increasing chimney and inlet widths significantly enhances ventilation performance by increasing airflow rate and ACH. Weather conditions and wind speed were also found to strongly affect thermal efficiency and buoyancy-driven airflow. Among the predictive models, XGBoost and DNN-IGWO exhibited the highest predictive accuracy, achieving coefficients of determination ($R^2$) close to unity and very low prediction errors for all output variables, confirming their robustness and generalization capability. The proposed methodology provides a reliable tool for rapid performance prediction and design optimization of solar chimney systems under different climatic and operating conditions, thereby supporting the development of energy-efficient passive ventilation strategies for residential buildings. Full article

SiO₂ aerogels are promising candidates for energy-efficient glazing because of their low thermal conductivity and optical transparency; however, conventional formulations often fail to reconcile optical, thermal, and mechanical performance. This work aimed to resolve this bottleneck via controllable sol–gel synthesis and ambient pressure drying. Using methyltrimethoxysilane (MTMS) as the single silicon source, this study systematically explored the effects of alkaline catalyst type, water-to-MTMS ratio, and surfactant selection, and further developed an MTMS–dimethyl dimethoxy silicane (DMDMS) composite silicon source. Tetramethylammonium hydroxide (TMAOH) catalysis, a water-to-MTMS molar ratio of 7:1, and Pluronic F-127 (F127) surfactant yielded a uniform, hydrophobic aerogel with 93.50% porosity and 89.74% transmittance at 800 nm. The optimized composite system (MTMS:DMDMS = 9:1, 6 mL water, 2.0 g F127) enhanced compressive strength by 22.4% relative to pure MTMS aerogel, with 70.15% visible transmittance and thermal conductivity of 0.027 W/(m·K). These results demonstrate that multi-parameter formulation control can achieve a practical balance among mechanical robustness, optical transparency, and thermal insulation. This study provides a theoretical and process foundation for the engineering application of high-performance transparent thermal insulation materials. Full article

Buildings in warm–humid and hot–arid coastal climates experience continuous cooling demand due to high solar radiation, humidity, and extended cooling seasons. Reducing operational energy use and carbon emissions through improved early-stage design is therefore essential. This study investigates a simulation-based evolutionary optimization framework to evaluate energy-efficient design strategies for residential buildings across representative warm–humid and hot–arid climates. A prototype residential building was modeled in DesignBuilder using EnergyPlus and evaluated across four locations: Singapore, Miami, Rio de Janeiro, and Jeddah. Key variables included the window-to-wall ratio, glazing type, wall and roof constructions, cooling setpoint, and HVAC system configuration. An evolutionary search process based on the NSGA-II algorithm was applied to systematically explore high-performing building configurations using energy use intensity (EUI) and operational carbon indicators. The results indicate a consistent tendency toward boundary values within the defined parameter ranges. The window-to-wall ratios consistently approached the minimum tested value (20%), while the cooling setpoints approached the upper bound (26 °C) within the defined parameter ranges. This behavior highlights the influence of solar gains and operational temperature settings on cooling demand. Low-emissivity glazing and insulated envelope assemblies were frequently associated with improved performance. Miami achieved the lowest EUI among the high-performing configurations (75.08 kWh/m²·yr; 27.55 kgCO₂/m²·yr), while other locations showed higher demand due to climatic conditions. These findings emphasize the importance of parameter range selection and demonstrate the effectiveness of simulation-based evolutionary search methods in identifying high-performing configurations within defined constraints. Full article

This study systematically reviews the role of Artificial Intelligence (AI) and Machine Learning (ML) in supporting design decisions to improve energy efficiency in educational buildings, with particular emphasis on Saudi Arabia’s hot-arid climate. A PRISMA-based Systematic Literature Review was conducted using Google Scholar, ScienceDirect, ResearchGate, and the Saudi Digital Library for studies published between 2020 and 2025. Eligible studies included peer-reviewed articles and high-quality conference papers addressing AI/ML applications in building energy performance, optimization, or design decision-making in educational or comparable buildings. Studies published before 2020, non-peer-reviewed sources, irrelevant studies, papers focused solely on non-educational buildings without transferable findings, and studies lacking full-text access were excluded. The search identified 594 records, of which 37 studies met the eligibility criteria, resulting in a final sample of 37 reviewed sources. The review shows that ML models, hybrid methods, and multi-objective optimization techniques are increasingly used to improve energy performance and support early-stage design. The most influential variables include envelope properties, glazing, shading, lighting efficiency, HVAC systems, and renewable energy integration. However, major gaps remain, particularly the limited application of AI-driven optimization in Saudi educational buildings and the lack of real-world validation in hot-arid settings. This review provides a concise foundation for future AI-assisted design strategies aligned with sustainable educational building development and Saudi Vision 2030. Full article

Passive solar shading is an effective strategy for reducing building energy demand, but its performance varies with climate, façade orientation, and thermal inertia. This study develops a sequentially coupled framework that links geometric shading calculation, anisotropic window heat gain prediction, and indoor thermal balance analysis across low- and high-latitude scenarios. For the low-latitude case, the model identifies a stable engineering overhang depth of about 1.85 m under the reference design space and weather inputs, while preserving winter solar admission. When compared with an unshaded baseline case with the same envelope, glazing, weather file, and internal gain assumptions, the optimized dynamic shading configuration reduces annual cooling load by more than 42% in the Guangzhou case study. For the high-latitude case, coupling shading with thermal mass parameters improves annual energy performance, and the best tested configuration achieves an energy-saving efficiency of 37.83% with an annual heating load of 96.14 MWh in the Stockholm scenario. The uncertainty and sensitivity analysis reports deterministic quantitative ranges and representative cases: the low-latitude recommended depth remains within the 1.85–1.864 m engineering neighborhood, while the Stockholm sensitivity sweeps show heating-load reductions of approximately 32.2–34.1% and indoor temperature variation reductions of up to 60.5–78.3% across the tested thermal mass parameter ranges. The discussion also clarifies the influence pathways of literature-sourced PCM and thermal property parameters, especially latent heat, thermal conductivity, and effective heat capacity. The quantitative validation boundary analysis distinguishes internal verification, controlled baseline benchmarking, and the external EnergyPlus/IDA ICE or measurement comparison still required for calibrated prediction. The results support the framework as a model-development tool for comparing passive design strategies under clearly defined assumptions, validation boundaries, practical engineering limits, and deterministic sensitivity ranges. Full article

The growing demand for sustainable energy solutions in the built environment has increased interest in hybrid envelope retrofits that integrate vegetation systems with on-site photovoltaics (PVs). This study presents a comparative assessment of two integrated vegetation–PV envelope retrofit strategies for an educational building in a cooling-dominated hot-humid climate relevant to Nearly Zero Energy Building (NZEB) applications. A calibrated dynamic simulation model was developed to quantify annual net electricity savings, operational CO₂ emission reductions, and cost-effectiveness using the levelized cost of saved electricity (LCOS). Two configurations were assessed: a solar green roof and a façade system combining green walls with glazing-integrated photovoltaics (GIPVs), enabling a consistent evaluation of roof-based and façade-based hybrid systems under identical conditions. Both strategies deliver comparable energy and environmental performance. The solar green roof achieves annual net electricity savings of 231.0 MWh and avoids 163.3 tCO₂, while the green walls–GIPV system provides 228.3 MWh and 161.4 tCO₂. However, significant differences are observed in economic performance. The LCOS of the solar green roof is approximately 0.07 $/kWh, compared with 0.28 $/kWh for the façade-integrated system. The results demonstrate that vegetation–PV hybrid retrofits can effectively support NZEB pathways in hot-humid climates, while highlighting that the solar green roof provides a more cost-effective solution under the studied conditions. The study contributes a consistent, decision-oriented comparison of integrated vegetation–PV strategies, linking energy, environmental, and economic performance within a unified modeling framework. Full article

Achieving thermal comfort in social housing under variable and changing climates presents a critical challenge for sustainable building design and energy efficiency. This study develops a simulation-based multi-objective optimization framework to support early-stage design of climate-resilient social housing in Brazil, aiming to reduce thermal discomfort and associated mechanical conditioning energy demands. The goal is to identify building envelope configurations that minimize total construction cost while maximizing annual thermal comfort hours, thereby reducing the need for active heating and cooling systems. A reference single-room prototype is simulated in EnergyPlus for five cities representing distinct climatic zones. A wide range of construction alternatives for walls, roofs, slabs, and glazing are evaluated, with costs derived from the national SINAPI database and comfort assessed using the ASHRAE adaptive model based on operative temperature. The optimization, performed with the NSGA-II algorithm (via PyMOO), generates city-specific Pareto fronts that quantify the inherent trade-off between cost and comfort. Results show that optimal solutions range from approximately R$4800 to R$8900 in cost, achieving between 1350 and 3550 annual comfort hours, heavily influenced by local climate. Frequency analysis reveals that wall and roof assemblies are the most influential design variables. The proposed framework provides a transparent, data-driven decision-support tool for defining cost-effective, climate-adapted construction standards, contributing directly to sustainable housing policy, energy poverty reduction, and the development of resilient, low-carbon built environments aligned with the UN Sustainable Development Goals (SDG), particularly SDG 7 (Affordable and Clean Energy), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action). Full article

Artificial lighting accounts for roughly 30% of total electricity use in supermarkets and significantly affects product perception, customer experience, and purchasing behavior. Increasing the availability of natural light, combined with appropriate architectural energy retrofitting strategies, offers a major opportunity to reduce electricity demand. This study proposes a data-driven framework for evaluating energy retrofit strategies in commercial buildings, integrating Building Information Modeling (BIM) and Building Energy Modeling (BEM). A parametric methodology is used to evaluate multiple architectural retrofitting scenarios aimed at enhancing daylighting and reducing artificial lighting demand, while improving energy efficiency and environmental performance. The scenarios investigated include variations in skylight geometry and orientation, glazing type, photovoltaic integration, and advanced lighting controls. Three Key Performance Indicators (KPIs)—real energy effectiveness, lighting control performance, and environmental impact—are used to assess how design modifications influence energy use, indoor lighting quality, and environmental performance. The methodology is applied to three real food-retail buildings in Italy. Results show that lighting energy consumption can be reduced by up to 60% in scenarios combining LED technology with smart control systems, while total building electricity savings vary across case studies depending on building characteristics and usage patterns. Environmental impact reductions of approximately 15–20% are achieved, reflecting both operational and life-cycle improvements. The study demonstrates the potential of parametric architectural retrofitting to support multi-criteria decision-making for sustainable refurbishment of food-retail environments. Full article

Aging populations have intensified the demand for thermally comfortable and energy-efficient housing, particularly for elderly residents whose diminished thermoregulatory capacity renders them disproportionately vulnerable to indoor temperature fluctuations. Existing senior apartments in cold-climate regions frequently fail to meet age-specific thermal comfort standards, yet systematic retrofit optimization frameworks explicitly tailored to elderly occupants remain scarce. This study presents a data-driven multi-objective optimization framework for building envelope retrofitting, which is validated using on-site temperature measurements from a representative 1980s brick–concrete senior apartment building in Beijing. The framework integrates Latin Hypercube Sampling (LHS) for design space exploration, a Long Short-Term Memory (LSTM) surrogate model for simultaneous prediction of three performance objectives, and Non-dominated Sorting Genetic Algorithm II (NSGA-II) for Pareto-optimal solution generation, with final selection performed via a weighted Mahalanobis distance-based Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Optimization targets—annual energy consumption, indoor thermal discomfort hours, and retrofit cost—are parameterized using the age-sensitive comfort thresholds specified in GB 50340-2016. The LSTM surrogate achieved R² values of 0.91–0.93 across all objectives with training–testing differences below 0.02. The optimal retrofit package—Polyvinyl Chloride (PVC) Low Emissivity (Low-E) double-glazed windows (5 + 6A + 5), glass fiber roof insulation (65.25 mm), and Extruded Polystyrene (XPS) external wall insulation (65.39 mm)—reduces annual energy consumption by 47.1% (from 40,867 to 21,626 kWh) and annual thermal discomfort hours by 62.4% (from 2454 °C·h to 923 °C·h). SHapley Additive exPlanations (SHAP)-based sensitivity analysis further identifies wall U-value and roof thickness as the dominant performance drivers. A reproducible and computationally efficient pathway is provided by the proposed framework for evidence-based envelope retrofit decision-making in existing senior residential buildings. Full article

This study investigates the impact of the Urban Heat Island (UHI) effect on building energy performance and the optimization of passive design strategies in six Moroccan climate zones: Agadir, Tangier, Fez, Ifrane, Marrakech, and Errachidia. A computer simulation approach combined with multi-objective optimization using the NSGA-II algorithm was employed to improve energy efficiency while maintaining thermal comfort for a single-family house. The optimum solutions include several passive design parameters, such as insulation materials and thickness, glazing types, window-to-wall ratio (WWR), ventilation rates, shading devices, building orientation, and heating and cooling set point temperatures. The analysis was studied under both standard climate data and UHI scenarios to evaluate the impact of increased urban temperatures on building performance. The results show that under standard climate conditions, the optimal design can achieve up to 76% energy savings throughout all the climate zones, while Marrakech can save 67% and Errachidia 64%; however, under UHI scenarios, these energy savings dropped by 8–30% depending on the climate zone. For example, Agadir drops from 76% to 49% under a 5°C UHI scenario, and Marrakech drops from 67% to 56% under a 3.5 °C UHI scenario, highlighting the significant impact of urban overheating on buildings. These findings emphasize that integrating the UHI effect is essential for accurately assessing passive design performance and for ensuring that selected design solutions truly minimize energy consumption under realistic urban conditions, while also underscoring the importance of integrating passive design strategies into residential buildings. These strategies promote sustainable building practices in Morocco by reducing energy consumption and improving occupant thermal comfort. Full article

The construction sector’s transition to carbon neutrality requires innovative strategies to address the performance and cost challenges of advanced building designs, such as passive-oriented nearly zero-energy buildings. This study proposes an artificial intelligence-based multi-objective optimization framework to reduce both energy consumption and construction costs for residential building envelopes in Chengdu’s hot summer and cold winter climate. The framework uses the NSGA-II genetic algorithm within DesignBuilder to explore trade-offs between energy efficiency and economic cost. Key design parameters (wall insulation thickness, roof insulation thickness, and window glazing type) are optimized to obtain a Pareto-optimal front. A subsequent global incremental cost analysis of the non-dominated solutions identifies the optimal balance where significant energy savings are achieved before diminishing returns set in. The research results show that by combining the NSGA-II algorithm with the global incremental cost method in the Chengdu area, the parameters of the enclosure structure can be systematically optimized, and the optimal balance point between energy conservation and cost can be effectively identified. Based on this, an “energy-saving optimal—trade-off optimal—cost optimal” template set design path based on dual objectives of energy consumption and cost can be obtained, which is applicable to different demand-oriented engineering scenarios. This research provides a quantifiable decision-making basis for the design of buildings with passive design strategies that achieve near-zero energy consumption in hot summer and cold winter regions, helping to achieve the coordinated optimization of energy efficiency goals and economic feasibility, and promoting the reliable promotion and application of near-zero energy buildings. Full article

Accurately determining the thermal transmittance (U-value) of glazing systems plays a pivotal role in building energy conservation. This study establishes an explicit analytical model and conducts a systematic parametric analysis to elucidate the heat transfer mechanisms and key influencing factors governing the U-values of both single-pane and insulating glass. Based on fundamental thermodynamic principles and blackbody radiation laws, numerical iterative models are developed and validated against WINDOW and Fluent software simulations, with deviations consistently below 3.8%. A comprehensive parametric analysis quantifies the effects of glass thickness, cavity width, surface emissivity, and indoor/outdoor heat transfer coefficients. The results reveal that: (1) while U-values decrease approximately linearly with increasing glass thickness, they exhibit a non-monotonic relationship with cavity width, identifying an optimal cavity width of approximately 16 mm for air-filled insulating glass units; (2) surface emissivity exerts the most significant influence on the U-value, with cavity-facing surfaces demonstrating the greatest sensitivity (up to 81% variation), whereas outdoor surface emissivity shows negligible impact; (3) the U-value displays greater sensitivity to variations in the indoor heat transfer coefficient compared to outdoor conditions. Based on the parametric analysis under standard winter conditions, a preliminary design hierarchy is proposed for energy optimization: prioritize Low-E coatings on cavity surfaces, followed by cavity width optimization near 16 mm, and finally consider increasing glass thickness. The validated models and quantitative insights establish a benchmark calculation method for U-value analysis. These findings offer theoretical guidance and a prioritized optimization pathway for the preliminary design of energy-efficient glazing systems, particularly under standard winter conditions. Full article

This study integrates in situ Quantitative Infrared Thermography (QIRT) and Building Energy Simulation (BES) to optimize the energy performance of an existing multi-story residential building in Istanbul, Türkiye. QIRT was utilized to diagnose thermal anomalies at the interfaces of uninsulated walls, the RC skeleton and fenestration junctions, revealing significant thermal bridging and air infiltration while enabling the calculation of the Temperature Index (TI) at critical interfaces. A key finding of the non-destructive diagnostic phase was the discrepancy between in situ (U_INSITU) and theoretical (U_CALC) thermal transmittance values, providing an empirical baseline for subsequent optimization. A multi-objective analysis, employing genetic algorithms (GAs), was conducted to evaluate 192 retrofit combinations, involving three insulation materials at four thicknesses and 16 glazing types. The impacts on primary energy consumption, CO₂ emissions, and 30-year global costs (per EN 15459-1:2017) were quantified under volatile economic conditions. Findings indicate that the energy-optimal solution reduces primary energy by 53% and CO₂ emissions by 51%, while the cost-optimal configuration reduces global costs by 52% relative to the reference case. The Pareto analysis reveals a robust convergence between financial and energy efficiency targets, proving that deep retrofitting is an economically imperative strategy for achieving national decarbonization goals and the 2053 net-zero vision. Full article

This study presents a numerical investigation of the effects of atmospheric icing on the aerodynamic performance and power output of the NREL 5 MW reference wind turbine. In cold climate regions, ice accretion on wind turbine blades significantly alters the airfoil geometry, leading to aerodynamic degradation characterized by increased drag, reduced lift, and substantial power losses. Understanding these effects is therefore essential for reliable performance prediction and efficient turbine operation under icing conditions. To address this problem, numerical simulations were conducted on six representative blade sections using the FENSAP-ICE framework, which integrates flow field calculations, droplet transport, and ice accretion modeling within a unified computational environment. The analyses were performed under different atmospheric icing conditions, considering liquid water content values of 0.22 g/m³ and 0.50 g/m³ and ambient temperatures of −2.5 °C and −10 °C. The median volumetric diameter was fixed at 20 µm, and the icing duration was set to one hour for all cases, allowing for both glaze and rime ice formations to be systematically examined. The results reveal that ice accretion becomes increasingly pronounced toward the blade tip, mainly due to higher relative velocities and increased collection efficiency in the outer sections. Glaze icing conditions produce irregular horn-shaped ice formations and lead to severe aerodynamic degradation, whereas rime ice forms more compact structures near the leading edge and results in comparatively lower performance losses. The degraded aerodynamic coefficients obtained from the iced airfoils were subsequently incorporated into BEM-based power calculations, indicating that total power losses can reach up to 40% under severe icing conditions, with the outer blade sections contributing most significantly to this reduction. Furthermore, an economic assessment based on annual energy losses highlights the substantial impact of atmospheric icing on wind turbine performance and operational costs. Full article