Search Results (209)

The growing demand for sustainable urban infrastructure requires reliable and energy-efficient lighting solutions, particularly for urban parks characterized by dynamic energy consumption patterns. This study proposes an integrated analytical framework combining deterministic energy system modeling, a novel interaction-oriented metric, and machine learning techniques to evaluate hybrid photovoltaic (PV)–wind systems for urban park lighting applications. Multi-year hourly meteorological and lighting-demand data from millet gardens in Balıkesir and Çanakkale, Türkiye, were analyzed. A Hybrid Energy Synergy Index (HESI) was introduced to quantify the degree of concurrent contribution and coordination between PV and wind resources relative to lighting demand. The results show substantial demand variability, with an average daily demand of approximately 1609 kWh and peak values exceeding 25,600 kWh. Photovoltaic generation dominated total renewable energy production (92.95%), whereas wind energy contributed 7.05%. The average HESI value (≈0.113) indicated weak source coordination, accompanied by persistent energy deficits that occasionally exceeded −20,000 kWh. Reliability analysis revealed severe system inadequacy, with a reliability rate of only 0.000547 (0.055%). Within the evaluated design space, the highest-performing configuration consisted of 49 PV panels and 19 wind turbines; however, reliability improvements remained limited, indicating that capacity expansion alone is insufficient to ensure satisfactory performance. Machine learning models achieved high predictive accuracy for HESI forecasting (R² = 0.9902; MAE = 0.0027), while explainable artificial intelligence identified solar radiation and wind speed as the dominant environmental drivers. The results highlight the importance of integrating renewable energy capacity planning with energy storage support, improved source coordination, and adaptive energy management strategies to enhance the reliability of sustainable urban lighting systems. Full article

Urban canopy mitigates urban heat, yet how the spatial configuration of high- and low-canopy layers shapes population heat exposure across a national urban system remains insufficiently understood. Drawing on a panel of 369 Chinese prefecture-level cities for 2000, 2005, 2010, 2015, and 2020, this study constructs a population-weighted thermal-exposure metric—the Human Heat Exposure Index (HEI)—and stratifies urban vegetation into high- and low-canopy classes based on Chinese Land Cover Dataset (CLCD) land-cover types. Multiscale Geographically Weighted Regression (MGWR) and Extreme Gradient Boosting (XGBoost) with SHapley Additive exPlanations (SHAP)-based interpretation are combined to identify spatially varying associations and nonlinear marginal effects of stratified canopy patterns on HEI. HEI shows a persistent south–high, north–low spatial structure, with Global Moran’s I stable at approximately 0.85 throughout the study period. High-canopy edge density and cohesion are increasingly associated with reduced heat exposure in densely built regions, while low-canopy mean patch area and edge density retain explanatory power across all years through near-surface evapotranspirative regulation. The marginal cooling effect of vegetation strengthens appreciably only above an Normalized Difference Vegetation Index (NDVI) of approximately 0.6, and the apparent inflection ranges for impervious surface proportion and standardized solar radiation lie near 25% and 0.4, respectively. These findings suggest that in cities with high impervious loads, cooling-network connectivity and within-zone canopy configuration matter more than additional canopy area alone, and that planning targets should be calibrated to climate zone, city type, and existing surface conditions. Full article

Building-integrated photovoltaics (BIPV) provide an important pathway for expanding distributed solar generation in dense urban areas, but comparable evidence on roof–facade resources across different urban morphologies remains limited. This study develops a scalable workflow to estimate the technical BIPV potential of roofs and facades within standardized 3 km × 3 km urban-core windows in 10 representative Chinese cities. Building footprints, height-related attributes, and functional tags derived mainly from OpenStreetMap were audited, cleaned, and completed through a hierarchical imputation strategy. A 2.5D urban geometry model was then used to estimate annual solar irradiation on building envelopes, with shading, orientation, and sky visibility explicitly considered. The results show that inter-city variation in BIPV potential is not governed by sunshine duration alone, but is strongly shaped by building density, height structure, envelope composition, and roof–facade contribution patterns. High total potential and high envelope-use efficiency do not necessarily occur in the same cities, indicating that total supply capacity and spatial deployment efficiency should be evaluated separately. The analysis further shows that facade-led BIPV pathways may be important in high-density urban cores, but facade-related estimates are sensitive to height-data completeness and usable-facade assumptions. These findings suggest that urban BIPV planning should move beyond aggregate solar-resource ranking and adopt morphology-aware, surface-specific, and data-quality-conscious assessment frameworks. The proposed workflow is intended for early-stage screening and cross-city comparison and provides a basis for identifying differentiated deployment priorities for roofs and facades in urban solar energy systems. Full article

Accurate solar irradiance forecasting is important for efficient planning of solar energy systems, renewable energy integration, and data-driven energy management in smart cities. This becomes more essential in regions with limited measured data availability and varying climatic conditions, where reliable forecasting can support urban energy planning and smart grid operation. Pakistan faces a scarcity of available solar data and has varying climatic conditions, which makes it ideal for such a study. This study utilizes nine geographically diverse stations to develop a benchmark framework for direct one-step-ahead hourly solar irradiance forecasting. The dataset was subjected to data preprocessing, feature engineering, and multi-model evaluation. A staged approach was adopted for feature selection, starting from a base model comprising three input variables: extraterrestrial radiation, solar zenith angle, and relative humidity. Features were added in an incremental order, which resulted in an optimized four-variable input set through the addition of a lagged clearness index to the base model. The forecasting models evaluated in this study, using these input variables, were ANN, NAR, NARX, LSTM, GRU, SARIMA, and Prophet. Deep learning models outperformed the other considered approaches, with LSTM showing the best overall benchmark performance with an average RMSE of 92.93 W/m², MAE of 66.56 W/m², and R-Squared of 0.872. The performance trends were broadly consistent across the evaluated stations, indicating stable behaviour within the adopted dataset and experimental setup. The study shows that a compact and physically interpretable input feature set, used with recurrent deep learning models, provides an effective solution for hourly solar irradiance forecasting, especially in locations with varying climatic conditions. The proposed benchmark can support smart city applications related to distributed solar generation, energy-aware urban planning, and intelligent operation of renewable-rich power systems. Full article

Urban rooftop photovoltaic (RPV) systems are crucial for energy transition in the built environment. Although artificial intelligence (AI) has been widely adopted in this domain, existing studies remain methodologically fragmented and lack a workflow-oriented comparative synthesis. This study conducts a scoping review to systematically examine the methodological development and workflow evolution of AI-based urban RPV potential assessment. A total of 524 articles were initially retrieved from Web of Science and Scopus. In total, 48 peer-reviewed studies were selected through a structured screening process. The results reveal a clear transition from conventional machine learning toward deep learning, multimodal learning, and increasingly integrated hybrid workflows. Geometry-based, parameter-based, end-to-end estimation, and hybrid workflows were identified as the dominant workflow paradigms, reflecting different balances between automation, scalability, interpretability, and physical realism. The review further highlights challenges related to transferability, benchmarking heterogeneity, uncertainty propagation, and data dependency under heterogeneous urban conditions. Overall, this study provides a workflow-oriented synthesis and comparative analytical framework of AI-based urban RPV potential assessment through a workflow taxonomy perspective highlights future directions toward more generalizable, physically informed, and adaptive urban energy modelling frameworks for solar-integrated urban planning and built-environment decarbonization, and intelligent urban energy system development across heterogeneous urban contexts. Full article

Food markets represent a public good essential for urban supply and as intergenerational spaces supporting the small-scale economy, yet they face growing challenges in adapting to sustainability regulations and circular economy requirements. This study examines the current state of sustainability in Madrid’s municipal markets through interviews and questionnaires administered to market managers, analyzing building characteristics, renewable energy systems, passive savings strategies, and energy costs across different market typologies. Results reveal that in December 2025, only 9% of markets had solar thermal installations, while merely 11% were planning photovoltaic solar panel projects—figures insufficient to meet current EU energy efficiency mandates. The findings demonstrate a significant gap between existing infrastructure and the requirements of the Directive (EU) 2023/1791, which supersedes previous directives. These results indicate an urgent need for accelerated implementation of renewable energy systems in market buildings to achieve sustainability targets. The study contributes baseline data for developing intervention strategies that can reduce energy consumption and align Madrid’s market network with European decarbonization goals for 2030 and 2050. Full article

Rooftop photovoltaic (PV) potential assessments have advanced significantly through high-resolution geospatial methods. However, most studies remain focused on well-planned urban environments and primarily consider geometric or radiative factors, often neglecting material constraints and deployment realism in heterogeneous cities of the Global South. This study addresses these gaps by developing an automated LiDAR- and GIS-based methodology to estimate rooftop PV potential in Cartagena, Colombia, explicitly integrating cadastral constraints, geometric feasibility, and roof material exclusion. The workflow combines LiDAR-derived elevation data, parcel-based segmentation, slope and aspect filtering, and post-processing techniques to identify PV-suitable rooftops, validated against 482 manually delineated polygons. The optimal configuration (45° slope threshold; 0.25 m buffer) achieved RMSE values of 6.79° (slope) and 20.95° (aspect). A geometry-constrained panel fitting algorithm estimated 3,599,631 panels across 146,091 rooftops, representing 7.06 km² of suitable area. Compared to simple area-based methods, this approach reduced capacity estimates by approximately 15.3%, demonstrating the importance of geometric realism. A key contribution is the integration of asbestos-cement (AC) roof exclusion, which reduced suitable rooftop area by ~65%, resulting in a final capacity of 1,281,202 panels. Estimated annual generation decreased from 1891.9 GWh/year to 673.4 GWh/year, equivalent to supplying 53.4–126.8% of Cartagena’s households. The proposed methodology provides a scalable framework for realistic urban PV assessment and introduces a dual-purpose planning tool that enables authorities to both prioritize solar deployment and identify areas requiring roof remediation, supporting safer and more controlled energy transitions in developing-country cities. Full article

Urban areas in subtropical regions are increasingly exposed to heat stress as climate change intensifies extreme heat events. In high-density cities, urban renewal is widely implemented to upgrade aging building stock, yet its potential role as a passive heat adaptation strategy remains insufficiently understood, particularly for projects below environmental impact assessment thresholds. This study examines how urban renewal influences neighborhood-scale microclimates through a comparative analysis of six residential renewal cases using computational fluid dynamics (CFD) simulations. Pre- and post-renewal scenarios are evaluated to assess changes in wind environment and thermal conditions, with a particular focus on the spatial extent and distance–decay characteristics of renewal-induced effects. The results reveal a consistent distance–decay pattern of microclimate responses across all cases. The influence of urban renewal is strongest within 0–50 m, remains detectable up to approximately 100 m, and diminishes substantially beyond 100–150 m, indicating a clear neighborhood-scale impact radius. Ventilation performance improves systematically following renewal, while thermal responses are more heterogeneous. Localized cooling of up to 1.5 °C is observed in selected cases, whereas others exhibit negligible temperature change despite enhanced airflow. These findings demonstrate that improved ventilation alone does not guarantee thermal mitigation. Instead, thermal outcomes depend on the interaction between airflow, solar exposure, and surface thermal properties. Urban renewal can therefore function as a form of passive heat adaptation when morphological changes are coordinated with shading and surface design strategies. By quantifying the spatial limits of renewal-induced microclimate effects, this study provides empirical evidence for integrating microclimate considerations into neighborhood-scale planning. The identified influence radius offers a practical reference for climate-responsive urban renewal, particularly in high-density subtropical cities where incremental redevelopment plays a dominant role. Full article

Optimizing solar access is fundamental for developing ‘Sustainable Healthy Cities’ and ensuring occupant well-being in high-radiation climates like Egypt. This study establishes an environmental methodology to enhance urban sustainability by controlling solar exposure to facades to mitigate health risks and reduce energy demand. The methodology involved a verified simulation using Autodesk Revit with Insight, followed by a comparative analysis of 45 scenarios. These scenarios evaluated the impact of orientation, geometry, urban spacing, etc., on solar performance. Additionally, the paper discusses the prospective integration of Generative AI and algorithmic engines to automate solar access layouts, proposing a roadmap for future AI-driven sustainable urban planning. The results indicate that strategic adjustments in urban morphology significantly improve solar access levels, directly influencing indoor environmental quality. The findings serve as a scalable framework applicable to regions like Kafr El-Sheikh or adaptable to extreme climates like Aswan, aligning with the UN Sustainable Development Goals (SDGs 3 and 11). In conclusion, this study demonstrates that environmental simulation provides a pragmatic pathway for architects to achieve integrated sustainability and healthy urban standards. This research offers a foundation for future sustainability investigations into thermal comfort and non-linear interactions between urban variables to refine solar access strategies in diverse contextual conditions. Full article

Urban canyons, integral components of the built environment, significantly influence microclimatic conditions and thermal comfort. This review investigates their combined effects with green infrastructure on thermal comfort, offering a comprehensive framework for supporting urban design and greening strategies. The review is based on a structured literature analysis of peer-reviewed studies retrieved from major scientific databases (Scopus and Web of Science), following defined selection and screening criteria. Urban canyon orientation determines solar exposure and its interaction with prevailing wind patterns, affecting ventilation and heat dissipation. The urban canyon aspect ratio influences shading and airflow regulation, while their sky view factor moderates radiative cooling and daylight availability. Urban greening—encompassing street trees, green roofs, and vertical green walls—complements urban geometry by reducing air temperatures, enhancing evapotranspiration, and modifying local wind dynamics. Tree shading can reduce the physiological equivalent temperature in urban canyons, mitigating extreme heat stress. Key vegetative parameters, such as leaf area index and canopy density, are critical for quantifying cooling contributions. Key findings underscore the role of higher aspect ratios in enhancing shading and ventilation while they emphasize the critical influence of street orientation and sky view factor on microclimatic regulation. Vegetation emerges as a vital component, with tree shading contributing substantially to cooling effects and reducing physiological equivalent temperature. The beneficial synergistic interaction between urban geometry and vegetation optimizes thermal comfort. Tailored strategies based on urban canyon typologies balance urban development with environmental sustainability. The proposed framework provides actionable strategies for designing resilient and thermally optimized urban spaces, promoting climate-adaptive urban planning by addressing the dual challenges of the urban heat island and thermal discomfort in cities. Full article

Urban–climate interactions in a warming climate remain largely uncertain; therefore, it is crucial to realistically evaluate and project these feedbacks to establish effective adaptation strategies. This study investigates projected shifts in summertime urban–climate interactions over eastern North America by employing the GEM regional climate model coupled with the Town Energy Balance (TEB) scheme, driven by RCP4.5 and RCP8.5 scenarios for the 1981–2100 period. Evaluations for the current climate (1981–2010) demonstrate that the model simulates an urban-induced warming of 0.5–0.7 °C and a precipitation reduction of 0.2–0.4 mm/day with high fidelity. By the late 21st century (2071–2100), projections under the RCP8.5 scenario indicate a steady weakening of the summer mean Urban Heat Island (UHI) intensity by approximately 0.10 °C, with a more pronounced nighttime attenuation of 0.15 °C. Physically, this weakening is attributed to an enhanced urban-induced evaporative fraction, which limits solar radiation storage within the urban fabric during the day, thereby reducing the thermal energy available for post-sunset release. This UHI attenuation correlates strongly with localized increases in precipitation, particularly in coastal regions where urban-induced effects contribute 20–40% to the total precipitation rise. While this study intentionally utilizes static urban boundaries to isolate the specific sensitivities of current urban morphologies to global warming, these results emphasize that diverse climatological regions will undergo distinct urban–climate feedback changes, providing essential baseline data for resilient urban planning. Full article

Urbanization and climate change intensify urban heat islands and air pollution; therefore, street canyon building planning that accounts for road orientation, shading, thermal environment, and ventilation is crucial. This study uses numerical simulations to investigate how non-uniform wall and road heating affects airflow and pollutant dispersion in street canyons under varying Richardson numbers (Ri) and heating scenarios (windward wall, leeward wall, road surface). The results indicate that large wall–atmosphere temperature differences combined with low incoming wind speed (high Ri) make thermal buoyancy a dominant control on canyon flow and pollutant transport. Heating of the leeward wall and road surface enhances ventilation and pollutant removal (prominently when the Ri ≥ 0.49), whereas heating of the windward wall suppresses dispersion and increases concentrations (prominently when the Ri ≥ 0.12). For a north–south street, diurnal solar heating produces strong micro-environmental contrasts. With easterly winds, morning heating of the windward wall elevates pollutant levels, while afternoon heating of the leeward wall promotes dispersion and lowers concentrations. Specifically, compared with the isothermal condition, the turbulent exchange rate at the top of the street canyon is enhanced to 1.71~6.86 times, while the convective exchange rate is suppressed to 58%~83% in the morning and enhanced to 1.21~1.92 times. These findings suggest that urban planning should limit windward wall temperature rises via shading and greening; thus, single-sided sidewalk and greening layouts on the windward side are recommended. Full article

Urban rooftop photovoltaic systems represent a substantial yet still underutilized renewable energy resource, particularly in high-density residential environments. Accurate large-scale assessment of rooftop solar potential, however, remains challenging due to the complex geometry of urban morphology and the limited availability of high-resolution geospatial data. This study presents a large-scale methodological framework for estimating the theoretical photovoltaic potential of urban rooftop spaces using Unmanned Aerial System (UAS)-based digital photogrammetry and GIS-based spatial analysis. The approach integrates centimeter-resolution Digital Surface Models (DSMs) and orthophotos derived from fixed-wing UAS surveys with detailed rooftop vectorization and solar radiation modeling implemented in a GIS environment. The methodology accounts for rooftop geometry, surface orientation, slope, shading effects, and rooftop-mounted obstacles. The methodology consists of data collection of high-resolution RGB imagery suitable for detailed three-dimensional reconstruction. The images are captured with a UAS equipped with a S.O.D.A. 3D photogrammetric camera, creating a dense, georeferenced three-dimensional point cloud based on UAS imagery. Based on the point cloud, a high-resolution Digital Surface Model (DSM) was produced. Rooftop boundaries and rooftop-mounted structures were digitized on the basis of an orthophoto created from UAS imagery. The analysis workflow consists of solar modeling using ArcGIS Pro, including calculating the solar radiation. The next methodological step is to filter low radiation rooftops, steep slopes, and northern-oriented rooftops. Finally, we calculate the potential electricity production. The framework was applied to high-density residential districts in Sofia, Bulgaria, dominated by prefabricated panel buildings with predominantly flat rooftops. Drone applications in such studies are typically restricted to modeling individual roofs, which severely limits their scalability for district-wide evaluations. To overcome this, the study employs a specialized fixed-wing UAS uniquely certified for legal operations over densely populated urban environments. This platform rapidly maps large territories, ensuring consistent lighting and shading conditions that significantly enhance the accuracy of subsequent rooftop digitization. Furthermore, the resulting centimeter-level precision enables the exact vectorization of micro-rooftop obstacles. Capturing these intricate details is a critical innovation that effectively prevents the overestimation of solar energy potential commonly observed in conventional large-scale models. Solar radiation was modeled at the pixel level for a full annual cycle and filtered using photovoltaic suitability criteria, including minimum annual radiation thresholds, slope, and aspect constraints. Theoretical electricity production was subsequently estimated using zonal statistics and system performance parameters representative of contemporary photovoltaic installations. The results indicate a total theoretical annual electricity potential of approximately 76.7 GWh for the analyzed rooftop spaces, with an average production of about 34 MWh per rooftop and pronounced spatial variability driven by rooftop geometry and exposure conditions. The findings demonstrate the significant renewable energy potential embedded in existing urban rooftop infrastructure and highlight the applicability of UAS-based photogrammetry for high-resolution, large-area solar potential assessments. The proposed framework provides actionable information for urban energy planning, municipal solar cadaster development, and the strategic integration of photovoltaic systems into dense urban environments, particularly in regions lacking open-access high-resolution geospatial datasets. Full article

Rapid urban expansion and the growing spatial requirements of utility-scale photovoltaic (PV) deployment compete for the same category of land—flat, accessible, and high-insolation terrain—yet the scale, trajectory, and planning-sensitivity of this conflict remain poorly characterised at the regional level. This study quantifies the spatiotemporal competition between urban construction land and PV-suitable land in the Yangtze River Delta (YRD) from 2000 to 2020 and projects its evolution to 2030 under three development scenarios. Built-up areas were extracted for three epochs using a Random Forest (RF) classifier on the Google Earth Engine (GEE) platform, achieving overall accuracies of 87.7–94.5% and Kappa coefficients of 0.718–0.739. PV site suitability was evaluated through a hybrid Multi-Criteria Decision Analysis (MCDA) framework combining Boolean exclusion constraints with an Analytic Hierarchy Process (AHP)-based Weighted Linear Combination model; the weight structure was validated by a Consistency Ratio of 0.006, and a One-At-a-Time sensitivity analysis confirmed spatial robustness across threshold scenarios. Spatial overlay analysis reveals that the cumulative area of PV-suitable land occupied by urban built-up uses grew from 15,862 km² in 2000 to 23,872 km² in 2020, representing an incremental loss of 8010 km² over two decades. Future conflict was projected using the PLUS model, calibrated on 2010–2020 observed expansion and validated against the 2020 classified map (OA = 93.99%, Kappa = 0.91). Under the Business-as-Usual (BAU) scenario, 33,368 km² of currently open PV-suitable land faces urban encroachment by 2030; the Ecological Conservation Priority (ECP) scenario reduces this figure to approximately 30,767 km², while the Economic Development (ED) scenario yields a near-identical outcome to BAU, indicating that development velocity alone does not determine the spatial extent of conflict—the allocation of growth does. These findings provide a quantitative basis for designating energy-strategic reserve zones within national spatial planning frameworks and demonstrate that targeted spatial governance, applied at high-pressure locations, can substantially slow the erosion of the region’s solar energy land base. Full article

This study evaluates the rooftop solar photovoltaic (PV) potential at the building level in the tourism-rich districts of Northern Khyber Pakhtunkhwa (KPK), Pakistan, using advanced geospatial analysis to support renewable energy planning. By combining the Area Solar Radiation tool with detailed building footprint data, the study identified solar energy potential and prioritized areas for PV system installations. Results show that approximately 35% of the 1.29 million buildings analyzed are suitable for solar panels, with energy generation capacity varying by building size and district. Spatial analysis further highlighted Union Councils (UCs) where over 50% of buildings are solar-suitable, enabling precise targeting of renewable energy initiatives. The study underscores the importance of integrating local geographical and socio-economic data to enhance the feasibility and scalability of solar energy solutions in rural and urban settings and can be used to guide policy prioritization and funding decisions. This research demonstrates how geospatial analysis and open data can drive localized clean energy adoption, directly contributing to Sustainable Development Goal 7 by advancing affordable and sustainable energy solutions. Full article