Search Results (4,429)

Building-applied photovoltaics (BAPV) offer a practical retrofit pathway for the prefabricated social housing estates of Central and Eastern Europe. This study assesses the solar and photovoltaic potential of post-WW II housing districts in Poznań, Poland, through a multi-scale workflow spanning the city, district, and building levels. Measured municipal rooftop data for 336 residential buildings in four districts were combined with tree-inclusive parametric solar simulations, calibrated against the rooftop solar cadastre (normalized mean bias error of +0.4% after calibration), to select South Winogrady and two representative buildings. Three PV design scenarios were then evaluated in roof, facade, and combined configurations using a techno-economic model that incorporates manufacturer-warranted degradation, maintenance, inverter replacement, and a ±25% electricity price and installation cost sensitivity envelope. Roof configurations pay back in 6.6–7.2 years (30-year return on investment of 286–321%), facade systems are economically defensible only on the best-exposed surfaces (8.9–13.8 years), and all configurations remain profitable even under the pessimistic bounding case. ENVI-met simulations of the same scenarios show localized pedestrian-level reductions in the Universal Thermal Climate Index of up to 2.41 °C near the PV-equipped buildings, persisting under 2050 climate projections. The results provide a transferable evidence chain for prioritizing BAPV retrofits in standardized post-war housing stock. Full article

Cold-climate dwellings can face coincident electricity and domestic hot-water shortfalls in winter, when solar availability is at its lowest. This study evaluates an integrated residential system for Aomori, Japan, combining photovoltaics, evacuated-tube solar water heating, and battery storage with electrolysis, compressed-hydrogen storage, and a PEM fuel cell operated in combined-heat-and-power mode. Building on a screening-level annual-balance analysis, a coupled annual TRNSYS simulation with a 0.125 h time step resolved battery dispatch, electrolyzer part-load operation, hydrogen compression and finite storage, seasonal fuel-cell operation, and heat recovery. The results show that the principal value of seasonal hydrogen lies in improving winter supply adequacy, dispatchability, and heat recovery rather than annual conversion efficiency. Fuel-cell heat recovery increased the number of days satisfying the hot-water screening indicator—a daily mean tank temperature of at least 43 °C—from 221 to 332. A reserve-aware criterion identified a 225 W electrolyzer operating-power cap as the positive-reserve case; 205 W was near-cyclic with a negligible margin, whereas the original 475 W cap was substantially oversized. The hydrogen pathway remained markedly less efficient than direct photovoltaic and solar-thermal use, and the estimated storage hardware’s lower bound substantially exceeded the break-even capital ceiling supported by the annual operating value. Seasonal hydrogen can therefore strengthen winter energy adequacy and heat recovery but is not yet cost-effective at the single-dwelling scale under the investigated conditions. Full article

This study addresses the challenge of incomplete indoor environmental monitoring data under alternate-floor sensor deployment in multi-story residential buildings. To enable cost-effective environmental sensing, a Building Environmental Data Reconstruction Framework (EDRF) is proposed for estimating unmonitored floor conditions. The EDRF integrates a convolutional neural network (CNN) for spatial feature extraction, a temporal convolutional network (TCN) for temporal dependency modeling, residual connections for stable feature propagation, and a multi-task learning (MTL) strategy for simultaneous reconstruction of multiple environmental variables. The model is trained and validated using real-world data collected from Floors 2–10 of a residential building, focusing on illuminance, temperature, and relative humidity. Experimental results demonstrate that the proposed framework achieves high reconstruction accuracy, with average R² values of 0.992 for temperature and 0.988 for relative humidity. Even under a reduced sensor deployment rate of 33.3%, the model maintains robust performance with an overall R² of 0.987. Ablation studies further confirm the effectiveness of each component in improving reconstruction accuracy. The proposed method provides a practical and scalable solution for reconstructing missing indoor environmental data and supports low-density sensor deployment in building monitoring systems. Full article

The construction sector accounts for around 40% of global energy usage and surpasses 36% of carbon emissions, highlighting the urgent need for improved renovation strategies. This research presents an AI-enabled generative design optimization framework that facilitates concurrent multi-objective optimization of architectural design, structural efficiency, and energy performance. The framework employs a 20-variable parametric design space and integrates a hybrid NSGA-III, a reference-point-based many-objective evolutionary algorithm with particle swarm optimization. Machine-learning surrogate models accelerate physics-based simulations by 500–850 times while maintaining prediction accuracy above 95%. The framework is validated through twelve renovation case studies comprising eleven residential and one office building spanning seven European countries, sourced from IEA SHC Task 37 and Passivhaus Institut databases, and calibrated to ASHRAE Guideline 14 standards (CVRMSE ≤ 18.6% across all buildings). The results evidence average reductions of 84.7% in operational energy consumption and enhancements of 20.1% in material efficiency, while consistently attaining a net-zero annual energy balance. Climate conditions significantly influence optimal insulation requirements, with a 37% difference between continental and Mediterranean regions. This study presents a scalable and computationally efficient method for AI-driven renovation design, overcoming the constraints of sequential approaches and facilitating substantial decarbonization of the built environment. Full article

Roofing systems strongly influence the energy performance and environmental footprint of buildings, particularly in hot–arid climates such as Saudi Arabia, where cooling dominates electricity demand; however, the comparative lifecycle environmental performance of alternative roofing strategies remains underexplored in this specific climatic and market context. This study therefore aims to evaluate and compare the environmental performance of four sustainable roofing strategies against a conventional flat roof (FR) baseline in order to provide evidence-based guidance for climate-specific roofing selection in Saudi Arabia. This study conducts a comparative cradle-to-grave lifecycle assessment (LCA) of four sustainable roofing strategies considering the hot–arid climate of Saudi Arabia. Green roof (GR), cool roof (CR), solar photovoltaic roof (SPV), and roof canopy (RC) were assessed using the ReCiPe 2016 method in the SimaPro software. The environmental impacts of these strategies were assessed across product, construction, use, and end-of-life stages relative to conventional flat roofs (FRs). The results indicate that the production stage consistently contributes the highest environmental impacts, with increases ranging from 30 to 3000% for GR, CR, and RC and exceeding 10,000% for SPV. On the other hand, the use stage offers the greatest reductions ranging from 10 to 200%, particularly for SPV and CR, due to operational energy savings and electricity generation. Overall, CR demonstrates the most balanced environmental performance, combining high impact reductions with minimal trade-offs, while SPV provides significant climate and fossil resource benefits but increases mineral resource use. These findings highlight the importance of climate-specific and resource-conscious selection of roofing strategies in Saudi Arabia and provide a transferable comparative LCA framework that can inform sustainable roofing decisions in other hot–arid and hot–humid regions, in support of the Kingdom’s Vision 2030 objectives for sustainable urban development. Full article

The present research investigates the relationship between urban regeneration initiatives and residential real estate market dynamics by assessing market price appreciation associated with the factors most commonly considered in housing transactions. The study focuses on the Esquilino district in the city of Rome (Italy) with particular attention to the redevelopment of Piazza dei Cinquecento, the major public space located in front of Roma Termini railway station. The intervention aims to improve urban accessibility, reduce traffic congestion, and enhance public space quality through a new spatial configuration and the creation of a tree-lined area. The objective of the study is to verify whether, and to what extent, the ongoing regeneration project has influenced residential property values. To achieve this goal, an econometric analysis is implemented to quantify the contribution of different housing and locational attributes to residential asking prices and to identify the variables that significantly affect value formation within the local market. Given that the initiative is still in progress and approaching completion, the analysis adopts a diachronic perspective by comparing two distinct temporal stages: the ante project phase (second half of 2021) and the in itinere phase (first half of 2025). Building on the findings of a previous pre-intervention study, the research systematically examines changes in market behaviors over time, with the dual purpose of identifying variations in price determinants and analyzing the associations between the current urban transformations and the residential real estate market. The results indicate a substantial stability in the main determinants of residential property prices across the two periods, suggesting that the regeneration initiative has not yet been fully capitalized into market behaviors. However, variations in the contribution and functional relationships of some spatial variables highlight preliminary signs of market adjustment during the ongoing transformation process. The study highlights the importance of monitoring for assessing how urban regeneration processes are progressively incorporated into real estate market dynamics. The proposed framework provides a transferable tool for evaluating regeneration processes in different urban contexts, supporting evidence-based decision-making and the comparative assessment of urban transformation strategies. Full article

Continuous monitoring of Activities of daily living (ADLs) requires sensing systems that are privacy-preserving, low-power, and robust to environmental variation. Ambient sensing technologies provide an alternative to RGB video and wearable devices, but individual sensing modalities exhibit characteristic limitations. Sparse mmWave radar provides strong motion sensitivity but limited posture detail, low-resolution thermal sensing preserves posture-related spatial information, and smart plug telemetry captures only appliance-mediated behavioural interaction. To address these limitations, this paper proposes a layered multimodal ambient-sensing framework comprising a sparse-track 24-GHz FMCW radar, a $32\times 24$ low-resolution thermal sensor, and a Moko smart plug. It experimentally evaluates a radar–thermal HAR branch together with a separate smart plug appliance-recognition branch. The framework proposes three streams to enable continuous non-wearable monitoring while maintaining redundancy and reduced privacy exposure for intelligent-building and ambient assisted living environments. Radar and thermal streams are jointly evaluated on binary motion and four-class posture and activity recognition tasks collected across multiple environmental configurations using recording-grouped cross-validation, while the appliance stream is evaluated using per-plug telemetry from residential-grade appliances. The radar–thermal streams use a single-subject, fixed-placement dataset of binary-motion windows and four-class posture and motion windows collected across six furniture configurations. The separate intrusive load monitoring stream utilises smart plugs to classify appliances. Regarding binary motion recognition, radar $(F_{1,\mathrm{Transformer}}=0.882\pm 0.034)$ and thermal $(F_{1,\mathrm{XGBoost}}=0.870\pm 0.069)$ pipelines achieved similar macro F1 performance. On the four-class posture and activity recognition task, thermal features $(F_{1,\mathrm{thermal}}=0.775\pm 0.053)$ substantially outperformed radar $(F_{1,\mathrm{radar}}=0.609\pm 0.110)$. Weighted late fusion produced only modest descriptive gains. Separately, smart plug telemetry demonstrated strong appliance recognition performance using lightweight tree-based models suitable for constrained edge deployment. The results support a scoped redundancy argument. Sparse track-level radar carries gross motion, while low-resolution thermal sensing carries posture. The smart plug appliance monitoring extends the framework toward appliance-mediated instrumental activity of daily living (IADL) monitoring, with lightweight tree-based models achieving strong recognition performance under constrained edge deployment conditions. The findings support a layered multimodal sensing architecture for privacy-preserving ADL monitoring, where radar contributes motion-sensitive coverage, thermal sensing contributes posture-aware spatial context, and smart plug telemetry contributes appliance-level behavioural evidence within intelligent healthcare and ambient assisted living environments. Full article

Rural houses in cold regions of China usually have high energy demands, particularly for space heating and domestic hot water. Passive design can reduce building energy demand, but additional renewable energy systems are still needed to improve on-site energy supply. This study evaluates the performance and limitations of photovoltaic (PV) and solar water heating (SWH) systems in a passive-designed rural house in Xi’an, China. Hourly simulations were conducted for PV-only and PV–battery configurations with different south-facing roof coverage ratios and battery capacities, together with an evacuated-tube SWH system. The results show that PV electricity supply was limited by the mismatch between household electricity demand and PV generation. Household demand mainly occurred in the morning and evening, whereas PV generation was concentrated around noon. The 13 m² PV case achieved approximately 11% electricity supply capacity with a utilization ratio of 62%, while increasing the PV area to 50 m² raised the supply capacity to only 15% and reduced the utilization ratio to 23%. With battery storage, the largest PV–battery configuration supplied 48% of annual household electricity demand, while the overall electricity utilization ratio was 73%, indicating a trade-off between household electricity self-supply and system utilization. The SWH system showed better applicability for domestic hot water supply, with an annual average hot water supply capacity of 60.2% and an average device efficiency of 43.5%, but its winter performance remained weak. These results indicate that PV and SWH are useful but insufficient solar energy strategies for passive-designed rural houses. PV is mainly constrained by daily time mismatch, while SWH is mainly constrained by seasonal climate variation. Full article

As urban areas grow and face climate-related challenges, rooftop greenhouses (RTGs) offer a promising approach for Controlled Environment Agriculture (CEA) to enhance urban food production while providing potential energy-saving benefits. However, the energy performance of RTGs integrated with mid-rise residential buildings, which represent a substantial proportion of the existing urban housing stock in cold climates, remains poorly understood. This study investigated the influence of RTG integration on the energy demand and indoor thermal conditions of a four-story residential building across cold-climate regions. Dynamic simulations using IDA-ICE were performed to analyze the influence of different glazing and shading configurations, comparing rooftop and ground-based greenhouse scenarios and host building performance. RTG integration reduced the host building’s annual heating demand by 6.2–7.6%, with thermal buffering primarily benefiting the top floor, where heating demand decreased by up to 24.2%, while lower floors remained largely unaffected. No summertime overheating risks were observed. Compared to a ground-based greenhouse, the RTG had a lower heating energy use by 2.8–14.6% depending on glazing and shading configuration, by using free heat from the host building. This study highlights the potential of RTG integration as a passive energy-saving measure, while supporting sustainable urban food production, contributing to more sustainable and climate-resilient cities. Full article

Implementation of real-scale systems for rainwater harvesting, treatment and reuse of greywater in residential areas is challenging because several factors should be considered for full adoption and satisfaction of decision-makers, urban developers and users. Technological, construction, operational, social (acceptance), impact on water resources, regulatory, and economic factors are involved. This study presents the implementation of a full-scale hybrid system for rainwater harvesting, treatment and reuse of greywater in a residential building located in Monterrey, Nuevo León, Mexico. The study included intervening in the hydraulic infrastructure of an already constructed residential building for collecting greywater, harvesting and collecting rainwater, designing and constructing an 80 m² controlled natural soil treatment system (CNSTS) and a 65 m³ storage tank for treating and storing rain and greywater. Furthermore, the full-scale hybrid system was monitored under real operating conditions for a two-month period to assess its performance. Results showed that the CNSTS has the potential to replace up to 2835 m³ year⁻¹ of potable water, equivalent to 65% of the building’s annual water consumption. The CNSTS achieved removal efficiencies of up to ~90% for Chemical Oxygen Demand, 90% for surfactants, and 50% for total nitrogen. Most of the measured parameters complied with the corresponding limits established by the Mexican standards NOM-003-SEMARNAT-1997 for non-potable water reuse, NOM-001-SEMARNAT-2021 for wastewater discharges, and NOM-127-SSA1-2021 for potable water with the exception of methylene blue active substances (surfactants), which exceeded the permissible limit during the initial monitoring stage, highlighting the need for further optimization of the system’s vegetative cover. Based on these findings, conceptual designs and preliminary evaluations were conducted for additional buildings, resulting in potable water substitution rates above 90% with investment payback periods of 2 to 5 years, depending on the water demand and the water catchment potential. Full article

Extreme heatwaves intensify urban heat islands and pose increasing risks to urban sustainability and human health. However, how urban morphology is associated with daytime and nighttime land surface temperature (LST) across urban functional zones (UFZs), particularly under heatwave conditions, remains insufficiently understood. To address this gap, this study integrates daytime and nighttime LST data derived from SDGSAT-1, multi-dimensional urban morphology indicators, and two interpretable ensemble models (XGBoost and GWRF) to investigate overall sample-level nonlinear model-based associations between urban morphology and LST and to explore spatial variation in local predictor importance within Beijing and Nanjing, China. Because the daytime and nighttime scenes were not always paired within the same heatwave episode, the analysis focuses on selected heatwave-condition observations. The results show marked contrasts between the selected daytime and nighttime observations in UFZ-level thermal patterns. Industrial zones generally exhibited the highest daytime LST, whereas residential zones showed the highest nighttime LST. Building density was identified as the primary model-based predictor of daytime LST in both cities, although its association with LST was nonlinear and varied across density ranges. In contrast, nighttime LST was characterized by more heterogeneous predictor associations, involving vegetation structure, sky openness, building form, anthropogenic indicators, and material-related variables, with their relative importance differing across cities and UFZ types. Local predictor-importance patterns also varied across neighborhoods, cities, and observation times, indicating that model-identified locally important predictors were not spatially uniform within each city. These findings highlight the potential of SDGSAT-1 daytime and nighttime thermal observations and interpretable machine learning for screening candidate local thermal priority areas and key morphology-related factors under heatwave conditions. Full article

Cities are confronted with heat, drought, and pluvial flooding, highlighting the need for renovation strategies that enhance climate resilience while minimizing greenhouse gas emissions. This research developed an integrated assessment framework to quantify the mitigation potential of urban and building surface components using five area-based key performance indicators (KPIs): Surface Heat Contribution (SHC), Flood Mitigation Factor (FMF), Water Storage Capacity (WSC), Evaporation Volume (EVA), and Global Warming Potential (GWP). The framework combines simplified life-cycle assessment with biophysical models for heat, water storage, and evaporation, designed as an accessible complement to 3D microclimate tools. Applied to an exemplary residential area in Daegu, South Korea, the method benchmarks existing surfaces and evaluates renovation scenarios targeting heat reduction, flood mitigation, and drought resilience. Results show that surface renovation measures improve mitigation potential. The performance varies across KPIs, involving trade-offs with embodied emissions. Optimal outcomes arise from balanced hybrid strategies integrating complementary measures. Selective greening combined with low heat capacity, high-conductivity materials (e.g., metal façades) effectively reduces heat stress but increases embodied GWP and structural demands. Permeable and greened surfaces improve WSC and EVA, supporting short-term flood mitigation, yet reveal limitations under prolonged rainfall. The proposed framework supports transparent and low-carbon climate-resilient renovation decision-making. Full article

Aging residential buildings constructed under outdated design standards create an urgent need for scalable, evidence-based indoor safety assessment methods. Conventional manual inspections rely on subjective checklists, lack audit trails, and are impractical for widespread deployment. This study presents a vision-based digital twin and AI agent framework that converts a single continuous smartphone video into an explainable, evidence-constrained safety assessment. The pipeline employs MASt3R-SLAM to reconstruct a metric-scale 3D point cloud from monocular video, calibrated with AprilTag fiducials for absolute scale. SpatialLM parses the geometry to extract semantic entities and spatial relationships. Risk guidelines are formalized into a computable Risk Prototype structure, unified within a hierarchical SceneState data structure that binds geometric measurements, semantic labels, image observations, and regulatory knowledge. A LangGraph-based AI agent conducts a dual-pathway assessment: an initial whole-dwelling scan followed by iterative follow-up queries invoking tool calls for measurement, knowledge retrieval, or visual cross-checking. In a pilot validation across five heterogeneous residences, with detailed manual comparison in two representative cases, the framework achieved risk recall rates of 77.8–100% and precision rates of 45.0–70.0% against the single-assessor manual reference. The average judgment closure rate was 71.7%, with spatial granularity enhancement of up to 2.2× in complex environments. These results suggest that the framework can achieve risk coverage comparable to manual checklist inspection while offering enhanced granularity in complex environments and quantitative precision in well-defined spaces. Full article

Foundation models for time series forecasting have recently been applied to energy prediction tasks, where they can produce accurate forecasts without task-specific training. This study investigates the impact of two types of covariates, meteorological variables and calendar-based day type indicators, on the forecasting performance of Chronos-2, a state-of-the-art foundation model, in building energy consumption prediction. Using real-world monitoring data from two non-residential buildings at the ENEA Research Centre in Portici, Italy, we systematically evaluate seven configurations combining past and future covariates across multiple observation window lengths (7–28 days). Future meteorological covariates are derived from historical weather forecasts rather than observed weather data, ensuring that the evaluation reflects realistic operational forecasting conditions. The results show that incorporating day type indicators as both past and future covariates consistently delivers the highest forecasting accuracy, reducing CV-RMSE from 14.58% for the covariate-free baseline to 10.41% with a 28-day observation window. A day-stratified analysis further reveals that these improvements are concentrated on regime transition days, for which recent load history alone provides limited information about the operating conditions of the day being forecast. By contrast, meteorological variables, whether obtained from weather forecasts or historical observations, yield only marginal performance gains, suggesting that calendar-driven operational schedules are the primary determinants of energy demand in the buildings considered. These findings provide practical guidance for deploying foundation models in real-world energy building management systems and show that covariate selection is a key determinant of forecasting performance. Full article

Background: Cognitive and functional difficulties commonly coexist in long-term care. Frailty and depressive symptoms may also co-occur with cognitive function and self-care dependence, but cross-sectional data cannot determine temporal order. We characterized the association structure among these measures. Methods: We analyzed complete standardized assessments from 182 residents of one long-term care institution. Cognitive function (Mini-Mental State Examination), physical frailty (FRAIL scale), depressive symptoms (15-item Geriatric Depression Scale), and self-care dependence (20-item Chinese Activities of Daily Living scale; higher scores indicate greater dependence) were assessed. Prespecified regression equations were adjusted for age, sex, and education. Path-product quantities and participant-resampling bias-corrected intervals (BC, not BCa) were used as descriptive stability summaries, not cluster-robust confidence intervals. Residential heterogeneity was examined with intraclass correlations and leave-one-building-out estimates. Results: Lower cognitive scores co-occurred with greater frailty, more depressive symptoms, and greater dependence. In the adjusted model, the total cognition–self-care association was c = −1.501 (BC interval −1.716 to −1.274), and the conditional association coefficient was c′ = −1.082 (−1.357 to −0.779). The frailty, depression, and ordered path-product quantities were −0.221, −0.166, and −0.033, respectively. Self-care dependence had a cohort intraclass correlation of 0.42. Across six building-omission analyses, the ordered quantity ranged from −0.050 to −0.018 and the frailty quantity from −0.272 to −0.164. Conclusions: These findings describe interrelated measures within one institution. They do not establish causal mediation, temporal order, or an intervention sequence. Full article