Rising temperatures disproportionately impact vulnerable communities in informal settlements of a... more Rising temperatures disproportionately impact vulnerable communities in informal settlements of arid Global South cities, yet data-driven frameworks for heat-resilient planning remain limited. This study pioneers an integrated machine learning (ML) framework—combining multivariate clustering, ensemble models (Random Forest, XGBoost, Gradient Boosting), and SHAP explainability—to analyze Land Surface Temperature (LST) dynamics in Kabul, Afghanistan. Results reveal informal settlements endure significantly higher LST (up to +5°C) than formal areas, driven by dense low-rise structures, minimal green space, and adjacent barren lands. While Gradient Boosting achieved the highest predictive accuracy (R² ≈ 0.45), the core contribution lies in translating ML insights into actionable planning strategies derived from urban morphological indicators (UMIs): (1) an optimal vegetation threshold (NDVI ≈0.15), (2) building heights around 3m to balance shade and ventilation, and (3) vertical densification for population management. Seasonal analysis highlights adaptive planning needs, with UMIs exerting stronger influences in summer but remaining relevant year-round. This research provides a replicable methodology for UMI-LST analysis in informal settlements, offering a pathway for equitable, climate-resilient urban development. We urge policymakers to embed targeted greening, managed densification, and land-use optimization into Kabul’s urban agenda.
Urban green spaces (UGS) are vital for well-being, yet equitable access within high-density Centr... more Urban green spaces (UGS) are vital for well-being, yet equitable access within high-density Central Business Districts (CBDs) remains critically understudied, despite acute health needs of office workers facing spatiotemporal constraints. This study addresses this gap by developing a novel, CBD-specific framework to assess UGS exposure and equity across five global CBDs (Shenzhen, Singapore, Melbourne, Tokyo, New York). This framework integrates: (1) An improved Gaussian Two-Step Floating Catchment Area (GA2SFCA) model quantifying daytime green exposure potential through NDVI-weighted supply and CBD-specific demand (derived from floor-area ratios), (2) A regional per capita exposure metric combining accessibility, quantity, and quality, and (3) Equity diagnostics using Gini coefficients, Lorenz curves, and bivariate local Moran's I. We apply this framework to five global CBDs (Shenzhen, Singapore, Melbourne, Tokyo, New York), revealing: (1) Significant exposure disparities: Shenzhen (highest) > Singapore > Melbourne > Tokyo > New York (lowest); (2) Equity variations: Lower inequality in Shenzhen (Gini = 0.33), Singapore (0.32), and Melbourne (0.29) vs. Tokyo (0.38) and New York (0.41); (3) Universal supply-demand mismatches via spatial clustering. This comparative analysis demonstrates how divergent urban policies yield distinct equity outcomes, with Shenzhen and Singapore offering models for effective CBD greening. Our framework provides planners with a transferable tool to advance environmental justice for high-density workplace populations. It advances beyond residential-focused models by capturing workplace-specific exposure dynamics.
As global climate change intensifies and energy resources dwindle, residential energy consumption... more As global climate change intensifies and energy resources dwindle, residential energy consumption becomes a critical challenge. While progress has been made in understanding urban morphology and climate factors in zero-energy residential block design, comprehensive cross-contextual analysis across diverse cultural, geographical, and climatic regions remains limited. This study examines residential blocks in eight cities—The Hague, Singapore, Tallinn, Berlin, Montreal, Vienna, Vantaa, and Zurich—spanning temperate oceanic, tropical rainforest, and humid continental climates. Using modeling translation tools such as Urban Modeling Interface and Rhino, alongside methods like multiple regression analysis, recursive feature elimination (RFE), and random forest algorithms, the study analyzes 12 morphological indicators from 240 districts in these eight cities. Morphological indicators serve as independent variables, while energy self-sufficiency rates are the dependent variable, with geographical and climate factors also considered. The results reveal that building height and volume are pivotal in determining energy self-sufficiency, particularly in northern cities like Tallinn and Vantaa, where optimized urban morphology can substantially enhance energy efficiency. In contrast, despite favorable photovoltaic conditions in Central European cities such as Berlin, Vienna, and Zurich, lower energy self-sufficiency rates are observed, mainly due to the constraints imposed by current urban morphology. For Singapore, optimizing the building shape coefficients and occupancy rates is essential for improving energy self-sufficiency. This study introduces a novel evaluation framework to assess and optimize the global potential of residential districts for achieving near-zero energy consumption, emphasizing the complex interplay between urban morphology and climate.
With the advancement of energy transition, residential photovoltaic (PV) systems face intermitten... more With the advancement of energy transition, residential photovoltaic (PV) systems face intermittency challenges that impact grid stability. While battery integration enhances resilience, existing approaches exhibit critical gaps: (1) underdeveloped hybrid modeling frameworks balancing physical interpretability and data-driven accuracy; (2) reinforcement learning (RL) strategies prioritizing economic gains over grid stability, risking localized fluctuations; and (3) performance evaluations lacking systematic assessment across varying PV-battery capacities. To bridge these gaps, this study proposes a hybrid framework combining physical energy flow constraints with XGBoost-based machine learning for robust forecasting. Two optimization strategies, proximal policy optimization (PPO) and rule-based control (RBC), are developed for charge-discharge scheduling, explicitly incorporating grid stability metrics. Multi-scenario analysis evaluates performance under varying capacities and initial states of charge (SOC). Results demonstrate the hybrid model's superiority over physics-based benchmarks, significantly improving prediction accuracy, with R 2 increasing from 0.70 to 0.95 for SOC and from 0.83 to 0.98 for grid power. Both PPO and RBC enhance efficiency and stability versus baseline: the energy self-sufficiency rate rises from 10.6% to 79.3% (PPO) and 82.4% (RBC), while grid power fluctuations decrease from 2.6 kWh to 1.66 kWh (PPO) and 1.38 kWh (RBC). Crucially, RBC achieves higher stability and interpretability near boundaries, whereas PPO excels in long-term optimization but exhibits boundary-condition sensitivity. Results further reveal that PV-battery capacity and initial SOC influence strategy performance. This study establishes a structured technical pathway encompassing hybrid forecasting model development, stability-oriented optimization design, and scenario-based performance evaluation, providing an integrated solution to enhance grid resilience and energy autonomy in residential PV-battery systems.
As urbanization increases, challenges like the urban heat island effect (UHI) and high building e... more As urbanization increases, challenges like the urban heat island effect (UHI) and high building energy consumption are becoming more significant. Roof retrofitting is a promising strategy to enhance energy efficiency and environmental quality in cities. However, most studies focus on single indicators and lack a comprehensive analysis of energy, environmental, and cost factors. This study uses a multi-criteria decision-making (MCDM) method with the TOPSIS algorithm to evaluate three roof retrofit scenarios—cool roof (CR), extensive green roof (EGR), and intensive green roof (IGR)—in Melbourne’s CBD. The analysis considers building energy consumption, microclimate benefits, life cycle cost (LCC), and technical feasibility. Results show that CR and IGR significantly improve energy efficiency, reducing annual energy demand by 0.73% and 1.5%, respectively. Green roofs, especially IGR, also provide notable environmental benefits, lowering air temperature 1.5 meters above the roof by up to 1.32°C. The TOPSIS assessment ranked CR first, followed by EGR and IGR, due to IGR’s higher cost and lower energy efficiency. After retrofitting, Melbourne’s CBD saw a 1.21% decrease in energy use intensity (EUI) and a slight reduction in roof temperature. This study’s framework provides a robust method for assessing roof retrofitting, offering valuable insights for urban energy and environmental policy.
Positive Energy Districts (PEDs) advance energy efficiency and renewable integration but often ov... more Positive Energy Districts (PEDs) advance energy efficiency and renewable integration but often overlook community energy resilience amid climate uncertainties. This study proposes a theoretical framework to address this gap, developed through a structured literature review and validated via a real-world case study. A systematic review of PED and energy resilience literature synthesizes methodologies—qualitative analysis, simulation modeling, resilience metrics—to identify gaps in existing approaches. Building on this, we design a structured framework integrating climate adaptability, multi-energy systems, and iterative resilience enhancement. The framework guides stakeholders through four phases: (1) defining PED boundaries using energy consumption patterns, (2) optimizing renewable capacity and storage, (3) simulating resilience under extreme weather via 3D and climate models, and (4) refining infrastructure using performance data. To demonstrate practical applicability, the framework is tested in a pilot case study. Initial parameters (building footprints, PV coverage, energy demand) inform baseline resilience assessments. Simulations of extreme climate scenarios reveal vulnerabilities, prompting targeted upgrades (e.g., expanded PV capacity, grid-enclosure measures). Post-intervention data show measurable resilience improvements, validating the framework's ability to balance renewable optimization with climate adaptation. By unifying literature-derived theory and empirical validation, this work shifts PED design from static energy targets to dynamic, resilient systems. The framework equips policymakers with actionable steps to future-proof communities, emphasizing energy security and renewable potential. It serves as a critical reference for urban decarbonization, bridging academic rigor and practical implementation to address escalating climate challenges.
As urbanization accelerates, optimizing outdoor thermal comfort in subtropical climates becomes c... more As urbanization accelerates, optimizing outdoor thermal comfort in subtropical climates becomes critical for enhancing quality of life. This study investigates the role of green space exposure in improving thermal comfort within small-scale subtropical campus environments, using Shenzhen University as a case study. We employed a stratified sampling strategy across 96 locations and 384 surveys, systematically capturing diverse microclimatic conditions and urban morphological features (e.g., vegetation density, building adjacency, surface materials) representative of subtropical campus settings. A machine learning framework integrating the XGBoost algorithm and Shapley (SHAP) value analysis was developed to decode complex interactions between climatic factors (air temperature, wind speed, solar radiation), physiological traits (respiratory rate, BMI), and green exposure metrics (Green View Index, GVI). Results demonstrate that green exposure significantly enhances thermal comfort, with optimal effects observed at GVI levels between 15 % and 56 %, beyond which benefits plateau. Crucially, individuals with lower respiratory rates and BMIs derived greater comfort improvements, particularly under high-temperature, low-wind scenarios. While findings are contextualized to subtropical campus environments-a critical niche for testing green infrastructure integration in high-density regions-the methodology offers a replicable framework for site-specific thermal comfort optimization. The study advances actionable strategies for campus planners, emphasizing context-sensitive green space design to mitigate heat stress. Future work should extend this approach to broader urban typologies, but this research underscores the value of leveraging localized climatic-physiological interactions in subtropical green space planning.
In response to increasing climate extremes, countries are advancing the development of Energy Com... more In response to increasing climate extremes, countries are advancing the development of Energy Communities (ECs) and Positive Energy Districts (PEDs). While homes and offices dominate PED research, schools—with their predictable schedules and underutilized off-hour capacity—hold untapped potential to enhance energy resilience through demand flexibility and solar surplus. This study proposes an optimization strategy for school-centered energy systems, integrating battery storage and surplus energy management to maximize emergency power provision and support peak-hour demand for schools and adjacent residences. Analyzing one year of energy data from Bear Creek High School (BCHS), we find summer surplus production (2941.6 kWh) offsetting winter reliance on external power due to heating and lighting demands. Residential energy peaks in autumn and winter further highlight the school's critical role in grid support. Annual simulations demonstrate a 45.6 % reduction in grid dependence, 3418 charge cycles, and $4.66 million in electricity cost savings. These results validate schools as strategic anchors for scalable PEDs, offering actionable insights for resilient urban energy planning.
Building energy efficiency is critical for carbon neutrality, yet rapid, large-scale performance ... more Building energy efficiency is critical for carbon neutrality, yet rapid, large-scale performance assessment remains challenging. This study introduces a three-stage multimodal framework combining Google Street View (GSV) imagery and GIS data to predict building-level Energy Performance Certificates (EPC), wall ratings, and Energy Use Intensity (EUI). First, facade extraction optimizes GSV camera viewpoints using GIS coordinates, segmenting key architectural features. Second, a deep learning model classifies wall performance (star ratings: very poor–very good) and EPC grades (A–G), incorporating shooting distance/angle analysis to enhance feature discrimination. Third, a pretrained energy model integrates building attributes with deep learning outputs, validated through transfer learning on 1 % local samples (e.g., 120 buildings) across UK cities. The framework achieves 84 % EPC accuracy and 76 % wall rating precision, with Grad-CAM interpretability identifying critical facade features—surface degradation, solar panels, and material patterns. Terraced houses (<7.95 m height) yield highest accuracy when facades occupy > 25 % of images, reducing regional EUI errors (CVRMSE) by 48–52 % versus conventional methods. Transfer learning adapts models to median-scale Middle Super Output Area (MSOA) blocks, outperforming in pre-1980 structures but facing limitations in urban high-rises due to occlusions. This scalable approach enables cost-effective retrofit prioritization and policy-driven decarbonization using widely accessible visual data.
Amid global housing shortages and high urban office vacancy rates, there is increasing advocacy f... more Amid global housing shortages and high urban office vacancy rates, there is increasing advocacy for converting offices into residential units; however, a dearth of a holistic framework for conducting environmental and socioeconomic impact analyses on such conversions remains. This study proposes a triple bottom line framework encompassing environmental, economic, and social dimensions to evaluate the benefits of repurposing urban office buildings into residential units through energy efficiency retrofits plus adaptive reuse. Using the Urban Building Energy Model (UBEM), the study simulates four scenarios for converting office space to residential units in Melbourne's CBD: 0%, 20%, 40%, and 100% conversion rates. Full conversion yields a 31.6% reduction in Energy Use Intensity (EUI) and annual carbon emissions decrease by 30,188 tons. Additionally, it projects an increase in apartment rental income of about $200 million per year, highlighting significant energy savings, emission reductions, and economic benefits. This adaptation could accommodate 10,000 to 12,000 new households, greatly enhancing social welfare and addressing urban housing challenges. This study also examines the relationship between EUI, peak energy demand, carbon emissions, and operating costs in relation to baseline building characteristics across four retrofit scenarios. The results indicate that buildings with high energy consumption offer the greatest potential for energy efficiency retrofits, but the benefits diminish as the proportion of residential functional conversion increases. This study not only furnishes urban planners and policymakers with a comprehensive decision support framework for assessing the necessity and viability of adaptive reuse but also provides essential practical guidance and a theoretical foundation for advancing sustainable urban development.
As global warming intensifies, frequent and severe heat waves increasingly threaten indoor therma... more As global warming intensifies, frequent and severe heat waves increasingly threaten indoor thermal safety, particularly for vulnerable populations such as the elderly. While existing research has advanced the understanding of building thermal performance and human heat stress under current conditions, significant gaps persist in two critical areas: (1) the development of holistic frameworks to assess building thermal resilience under future climate scenarios and (2) the integration of occupant-specific health risks, especially for aging populations, during concurrent heat waves and power outages. This study addresses these gaps by proposing a novel, scenario-based framework to evaluate thermal comfort and resilience in a near-zero energy house in California. Field data and an automatic calibration technique (genetic algorithm) were applied to develop a validated Building Energy Model (BEM), which was then tested under IPCC's SSP2-4.5 and SSP5-8.5 climate projections for around 2050 and 2080. Three scenarios—baseline (current conditions), ideal (adequate energy supply), and emergency (power outage)—were simulated to quantify overheating risks. Sensitivity analysis identified active cooling, natural ventilation, and ventilation volume as dominant factors influencing thermal performance. Results indicate that by 2080, under high-emission scenarios, annual thermal discomfort duration for non-elderly populations will escalate to 220-270 hours, while elderly populations face 90-140 hours. Notably, HIHH for the elderly surpasses critical health thresholds (160-235°C·h) during power outages, and the thermal resilience index declines by 62%, signaling prolonged severe overheating. This study underscores the challenges homes will face during future heat waves and provides a robust framework that can generate technical guidance for improving thermal performance and resilience.
Urban heat mitigation strategies are essential for enhancing microclimates and optimizing energy ... more Urban heat mitigation strategies are essential for enhancing microclimates and optimizing energy performance in residential areas. However, quantitative frameworks for renovating these areas are still limited. This study combines ENVI-met and DesignBuilder software to evaluate the effectiveness of vegetation, highly reflective materials, and their combined strategies in typical residential areas across four representative climate zones: The Hague (Cfb), Montreal (Dfb), Wuhan (Cfa), and Singapore (Af). The results reveal significant regional variations in the effectiveness of these strategies. In hot and humid climates, a combined strategy is the most effective, reducing air temperature in Wuhan by 0.68 °C and cooling loads by 54.7 %. Reflective materials, while increasing mean radiant temperature in all climates, reduce ground heat accumulation in milder climates like The Hague, resulting in a 2.94 °C decrease in surface temperature. Vegetation generally provides less ground cooling but outperforms reflective materials in energy savings across all climates. Furthermore, temperate cities experience higher energy consumption under heat mitigation strategies. The effectiveness of these strategies is also influenced by the physical characteristics of residential areas, such as building height and green infrastructure. Therefore, tailoring heat mitigation strategies to local climatic and urban conditions is crucial. This study identifies optimal strategies for various contexts, offering a scientific foundation for precise policy-making to address heat-related challenges. Moreover, the methodology employed in this research provides a fresh perspective, delivering more precise and comprehensive analyses that fill gaps in previous studies lacking direct global multi-environmental comparisons.
The residential sector accounts for a significant share of global carbon emissions, and energy ef... more The residential sector accounts for a significant share of global carbon emissions, and energy efficiency retrofitting of buildings is crucial for achieving carbon neutrality. However, assessing energy demand and determining retrofit priorities within large building stocks presents numerous challenges. This study proposes an innovative and simplified approach that reduces the complexity of evaluating large-scale residential building stocks by focusing on building prototypes, thereby effectively assessing regional energy consumption. The innovation of this method lies in the combination of Shapley values with clustering techniques to ensure that building prototypes are representative in terms of energy efficiency. This not only enhances the interpretability of clustering results but also improves their practical application in energy efficiency analysis. Taking England as an example, this study identifies six residential building prototypes and constructs an energy consumption model based on Level of Detail 1 (LoD1), using calibration to capture regional heterogeneity. The research also finds that factors such as climate, demographics, and income significantly influence EUI, and there are notable variations across different regions and building types. Moreover, if all homes in the UK were to achieve a C-grade in Energy Performance Certificate (EPC), it is estimated that approximately 60,922.85 GWh of energy could be saved, representing 17.4% of the total residential sector energy consumption in the UK in 2021. This study provides a framework for the effective allocation of retrofit resources and identification of high-potential energysaving opportunities.
Energy-related carbon emissions from the transportation sector are one of the major obstacles to ... more Energy-related carbon emissions from the transportation sector are one of the major obstacles to achieving global carbon reduction targets. Current research on transportation energy mainly focuses on the energy end-use stage, with a lack of simulations covering both the front-end and end-use stages of transportation energy. This study, from a life-cycle perspective, conducts a carbon emission simulation for county-level transportation. Based on the 2019 transportation data of Huadu District, Guangzhou, the study combines life-cycle assessment (LCA) with the Low Emission Analysis Platform (LEAP) to simulate changes in transportation carbon emissions from 2020 to 2050. The results indicate that: (1) The Energy-Saving scenario has the greatest carbon reduction potential, capable of reducing carbon emissions by 75 %. (2) Energy efficiency factors have the most significant carbon reduction effect in transportation. (3) There is a carbon transfer phenomenon from "Tank-to-Wheel" to "Well-to-Tank" in transportation energy. (4) Embodied carbon accounts for 40 %, while operational carbon accounts for 60 % of the entire transportation energy life cycle. Based on the above results, the study suggests that the government should increase policy support and technological innovation, invest in public transportation infrastructure, and strengthen carbon management throughout the entire lifecycle to comprehensively enhance the carbon emission reduction effects of transportation energy.
To address the building decarbonization crisis, the widespread adoption of rooftop photovoltaics ... more To address the building decarbonization crisis, the widespread adoption of rooftop photovoltaics (PV) has been agreed upon globally, with PV potential prediction being a crucial evaluation task. Current methods for predicting rooftop photovoltaic (PV) potential face significant shortcomings, as geospatial approaches struggle with precision at urban scales, historical time-series methods tend to overestimate potential, and urban studies often neglect spatial shading between buildings, thereby inflating predictions. This study addresses these issues by employing a Graph Convolutional Network − Long Short-Term Memory (GCN-LSTM) model to perform spatiotemporal predictions of urban rooftop PV potential, incorporating the spatial shading relationships between buildings to enhance prediction accuracy. The results show that, compared to traditional Long Short-Term Memory (LSTM) models, GCN-LSTM significantly improves prediction accuracy, reducing MAE by 21%, MSE by 22%, RMSE by 13%, and MAPE by 12%. This improvement is particularly evident in winter and summer, validating the interpretability of the GCN-LSTM model. Moreover, clustering analysis of the shading relationship graphs between urban buildings identified three primary types of graph clusters: moderately diverse medium-scale building shading graphs, simple small-scale building shading graphs, and complex large-scale building shading graphs. Factors such as the number of buildings, standard deviation of building heights, and standard deviation of roof slopes were found to collectively influence the complexity and shading intensity of these graphs, leading to variations in PV potential. Based on the findings of this study, it is evident that integrating deep learning models with engineering physics knowledge can substantially enhance the accuracy of urban rooftop PV potential predictions, providing suggestions and bases for the formulation and implementation of PV promotion policies.
The urban heat island effect and extreme high temperatures pose significant challenges to the liv... more The urban heat island effect and extreme high temperatures pose significant challenges to the livability of cities, making it urgent to mitigate urban heat issues. However, uncertainty remains regarding the most effective cooling measures. The differences between single and combined measures have not been clearly defined, and there is limited research comparing their impacts on urban microclimates and energy consumption. Therefore, this study aims to explore the differences between single cooling measures and mixed cooling strategies from the perspectives of temperature, thermal comfort, and building energy consumption to provide a more holistic impact analysis of cooling strategies. Therefore, this study conducted a simulation on a typical street canyon in Melbourne, Australia, on December 20, 2019. Four individual scenarios and six combined scenarios were established. Subsequently, the environmental parameters and building energy consumption were simulated under various cooling scenarios. The results indicated that the combined cooling scenario with three measures achieved the highest comprehensive score, demonstrating the most favorable impact on the thermal environment and building energy consumption. We also found that the green scenario combined with high-reflectivity can reduce building energy consumption by 71.83 kWh within a day, yielding the best results. However, the combination of green infrastructure, permeable surfaces, and misting proved to be the most effective in improving the environmental thermal comfort, with a maximum reduction of 1.86 °C in the UTCI (universal thermal climate index). This study can provide guidance and recommendations for designers in the early stages of decision-making.
Inhabitants of informal settlements face socioeconomic difficulties and suboptimal living conditi... more Inhabitants of informal settlements face socioeconomic difficulties and suboptimal living conditions, where demographic, architectural, and well-being factors interrelate to determine quality of life (QoL). This paper explores how these interactions occur and how architectural interventions can shape these environments in ways that contribute to improved health outcomes. We conducted a demographic and architectural study, with a focus on daylighting among selected inhabitants. We also investigated well-being by utilizing the SF-36 and Pittsburgh Sleep Quality Index (PSQI) surveys to measure health and sleep quality. The results clearly showed that older age and lower education are strongly related to poor health, while natural light exposure and better building orientation are strongly related to improvement in sleeping quality and, hence, good health. Distinct clusters emerged from this population; for instance, the employed and better-educated people presented higher well-being. Interaction effects also showed how age, education, and daylight exposure are interactively determining health and sleep outcomes. The health of the less educated elderly decreases at a much greater rate; accessibility to daylight moderates this. Such findings hint at targeted interventions that might involve optimal window placement, coupled with improvements in building orientation and social support measures to enhance general well-being among vulnerable groups. Merging these architectonic and socioeconomic factors, the interventions could contribute a lot to people living a better life in such informal settlements.
In recent years, the green building industry in Southeast Asia has shown a clear trend of develop... more In recent years, the green building industry in Southeast Asia has shown a clear trend of development. However, the development differences among Southeast Asian countries are generally large, and existing studies lack a comprehensive comparative analysis of green buildings in developed and developing countries in the region. This study takes Singapore and Vietnam as typical representatives of developed and developing Southeast Asian countries and compares their green building rating systems, green building technologies, and the current status of building energy consumption of various types of commercial buildings. The results show that there is a significant difference in the overall energy consumption levels between Singapore and Vietnam. The overall energy consumption level of buildings in Singapore is much higher. And probably due to the standardization of the green building rating system and advanced development of green building technologies in Singapore, the energy consumption level of all types of commercial buildings in Singapore is more stable and less fluctuating than in Vietnam. The results of the study critically point out differences in green building development and the current status of energy consumption between developed and developing countries in Southeast Asia and provide a direction for improving the existing green building evaluation standards, promote the development of green buildings in developing countries, and to narrow the regional gap.
Building facades, especially windows, are essential for indoor lighting and solar energy use, but... more Building facades, especially windows, are essential for indoor lighting and solar energy use, but traditional windows often fail to balance daylighting and energy performance, while semi-transparent building-integrated photovoltaics (BIPVs) offer a promising solution, though research on optimizing their design for energy harvesting, daylight sufficiency, and glare control in office spaces is limited. This study proposes a multi-objective optimization framework for designing semi-transparent building-integrated photovoltaic (BIPV) windows to balance energy efficiency, daylight quantity, and glare control in office buildings. Using a case study of an office on a campus in Wuhan, we developed a parametric model to control key design parameters such as photovoltaic window area and distribution. Simulations evaluated the impact on daylight sufficiency, glare, and photovoltaic efficiency. The results were analyzed using K-means clustering, revealing three design clusters: one maximizing transmittance with lower daylight and glare performance, another optimizing energy production and glare reduction, and a third offering a balanced performance with the best daylighting, though slightly reduced glare. The optimal design from Cluster 3 resulted in a 15.63 % increase in annual illuminance, improved daylight uniformity, and a 6.55 % increase in spatial glare autonomy, while maintaining sufficient daylight levels. The BIPV system generated 5,508.86 kWh annually, meeting the office’s entire nighttime energy demand. This framework provides valuable guidance for early-stage building design, helping to optimize both energy and lighting performance in BIPV-integrated facades.
To address office vacancy and housing shortages, adaptive transformations of office buildings int... more To address office vacancy and housing shortages, adaptive transformations of office buildings into residential spaces are proposed. While renovating old office buildings is prioritized, energy and socio-economic feasibility assessments are lacking. This study fills the research gap by using building performance simulations to create over 500 models, revealing the potential benefits of converting office buildings to residential use through multi-scenario evaluations. The research examined twenty-three pre-1980 office buildings in Los Angeles County with vacancy rates over 50%, establishing a baseline scenario based on actual data. Four scenarios were compared: baseline scenario, office energy efficiency transformation scenario, office-to-residential transformation scenario, and office-to-residential and energy efficiency transformation scenario. The study evaluated energy consumption and costs for each scenario. The research found that functional transformations cut energy use intensity by 46.09% and Life Cycle Cost by 61.61%, saving at least 5,154.84 MWh annually for Los Angeles County. It also found a comprehensive approach that reduces electric equipment loads, improves insulation, and moderately cuts lighting power density offers the best energy and economic benefits. The study suggests prioritizing renovation for small to medium-sized old office buildings with energy consumption below 348.3 kWh/m2·yr, footprint area over 2000 square meters, and height under 26 meters. This study highlights the benefits of functional transformation over energy-saving transformations in reducing operational energy and costs, offering decision-making methods and optimization recommendations for converting non-residential buildings to address the climate and housing crisis.
Uploads
Papers by Zhonghua Gou