Search Results (1,192)

The decentralized deployment of photovoltaic (PV) systems in urbanized polluted mountainous basins faces unique challenges due to complex topography, persistent cloud cover and winter smog conditions. This study quantifies the atmospheric impact of localized winter haze/smog and Saharan dust intrusions on PV performance in the intermontane basin of Ioannina, NW Greece. By integrating a Digital Twin (DT) methodology with real energy production data, two PV plants were evaluated, a ground-based and a rooftop installation, to isolate the energy deficits caused by aerosol attenuation. The DT model demonstrated high accuracy (R² = 0.847) against actual power generation data for Koutselio and R² = 0.865 for Mpafra PV plants, while MBE was near zero for both sites (−0.008 kWh and −0.139 kWh, respectively). Error analysis revealed that the highest modeling discrepancies occurred during scattered clouds and intense winter haze conditions, primarily due to low spatial resolution of CAMS that fails to adequately capture localized biomass burning (ΒΒ) events. Despite the reduction in direct sunlight during extreme winter BB events, results indicate that the overall energy loss is mild. This operational stability is primarily due to the ability of c-Si modules to effectively utilize near-infrared radiation, which penetrates the low-level haze layer, alongside the thermal efficiency gains provided by low early-morning temperatures. Crucially, the installation geometry may influence system vulnerability. Direct comparisons revealed a minor power deviation of –4.8% for the ground-based Koutselio plant, while for the Mpafra site, there was a +3.2% production surplus likely linked to the high sky-view factor the rooftop installation has, which manages to capture isotropic diffuse irradiance. However, the low CAMS resolution may misclassify the haze events within the basin, further contributing to these discrepancies. On the contrary, Saharan dust intrusions caused broadband light attenuation, dropping the power production significantly on both installations. Ultimately, this research provides critical insights into the resilience of solar systems under strong air pollution events within polluted valleys in Southern Balkans, highlighting the connection between panel design and atmospheric attenuation. Full article

Papua New Guinea’s capital, Port Moresby, is growing rapidly, and housing development has not kept pace. Thousands arrive in the city each year from rural villages, and many come to live in the city’s sprawling urban squatter settlements. The National Capital District Commission lacks reliable population data for these so-called “self-help” settlements, posing a challenge for urban planning and the allocation of scarce development resources in a fast-growing city. Utilizing publicly accessible geospatial data gathered from 18 years of aerial imagery (top-down) alongside insights from on-the-ground interviews (bottom-up), this project establishes a low-cost, mixed-methods approach for estimating population and change within a large informal settlement. The findings show how manual rooftop identification, combined with local qualitative validation, can produce robust settlement-level population estimates in data-scarce environments and illustrate context-specific limitations of automated Earth Observation-based population models in Melanesian cities. This approach is applied to 8-Mile: a well-established 2.5km² settlement northeast of Port Moresby’s city center. Results demonstrate the practicality of this method and reveal that, despite evictions and commercial rezoning, the settlement is growing at close to double the national average and is currently home to at least 27,000 people. Overall, the study highlights the value of combining spatial analysis with community-level knowledge to support evidence-based urban governance in rapidly growing cities. Full article

Three-port DC-to-DC converters based on the SEPIC-boost circuit have gained remarkable attraction in standalone applications such as DC micro grids having PV panels as roof-tops in electric boats, electric and hybrid vehicles, LED driving circuits, telecommunication systems, and medical and industrial electronics devices. Such a combination of SEPIC-boost in a single converter eliminates the use of three separate DC-to-DC converters to charge the batteries and to supply power from the PV module or batteries to the load. All such modes of operation in a single package make the converter compact by reducing the number of solid-state devices and passive components. It also enables the reduction in conversion losses and hence improves the system’s overall conversion efficiency. The control of a single circuit becomes simple and effective in terms of power management by detecting the solar irradiation and state of charge (SOC) of the battery. It enables the continuous flow of power to the load from PV modules or batteries, which is determined by the SOC of the battery and the available level of solar irradiation. This article develops the dynamic or state-space modeling of the combined operation of the SEPIC-boost-based DC-to-DC converter, which has not yet been developed in the literature. The development of systems based on separate dynamic SEPIC or boost modeling cannot meet the requirements of all operating modes. A state-space model of the combined operation of the SEPIC-boost converter enables evaluating the performance of such an energy management system during its various operating modes effectively. The validity of the developed model is recognized with results gained from MATLAB/Simulink and electronics-based Multisim computer software. Full article

Achieving carbon neutrality toward 2050 requires not only the adoption of renewable energy technologies but also long-term planning based on their gradual integration into urban systems. This study presents a novel scenario-based framework that quantifies year-by-year impacts of deploying rooftop photovoltaics (PVs), electric vehicles (EVs), and Vehicle to Home (V2H) systems from 2018 through 2050. Five key dimensions are assessed: CO₂ emission reduction, cost savings, self-consumption, energy sufficiency, and self-sufficiency. Using Suita City, Japan, as a case study, we analyzed two diffusion scenarios, “baseline (current growth case)” and “accelerated deployment case”. The results indicate that despite high initial investment, an accelerated scenario can achieve up to a 60% CO₂ emission reduction and over 25% cost savings by 2050. Importantly, the integrated deployment of PVs, EVs, and V2H surpasses PV-only systems in overall effectiveness from around 2035 onward. By capturing the dynamic evolution of technology impacts over time, this study provides unique and actionable insights for designing data-driven policies supporting long-term urban decarbonization. Full article

Reducing carbon emissions while carrying out urban renewal has put existing residential buildings in the spotlight for low-carbon transformation. These buildings typically consume large amounts of energy and offer significant savings potential, making them a priority in the building sector. Addressing the challenges of limited capital, long payback periods, and inadequate comprehensive benefit assessment in building energy retrofits, this study introduces a carbon trading mechanism and develops a priority decision-making framework based on life-cycle cost–benefit analysis and net present value rate (NPVR). Five typical retrofit measures (grouped into four simulation categories), including external wall insulation, roof insulation, window replacement, lighting upgrade, and rooftop photovoltaic (PV) system, are evaluated through TRNSYS energy simulation applied to an aging residential building in Xi’an, China. The results demonstrate that lighting system upgrades and rooftop PV installation yield the highest economic returns and investment efficiency, while building envelope insulation measures, despite delivering substantial energy savings, exhibit lower NPVR due to high initial investment. Sensitivity analysis reveals that electricity price is the dominant factor influencing economic viability, whereas carbon price under current market conditions exerts limited influence on retrofit prioritization. The proposed framework provides a quantitative decision-support tool for building owners and policymakers to optimize retrofit investment strategies under budget constraints. Full article

The rise in energy demand in Thailand due to constant economic growth coupled with reliance on limited natural gas and oil resources has led to an increased demand for alternative sources of energy. Therefore, this study aims at examining factors that influence the intention of adopting solar rooftop energy among households in Thailand through the integration of the theory of planned behavior (TPB) and the technology acceptance model (TAM). A quantitative research approach was adopted whereby data were obtained from 255 households in all parts of Thailand through questionnaires. The results reveal that attitude (β = 0.484, p < 0.001), perceived usefulness (β = 0.271, p < 0.05), and subjective norms (β = 0.257, p < 0.001) positively and significantly influence intention to use solar rooftop energy, collectively explaining 61% of the variance (R² = 0.61). Attitude proved to be the most significant predictor in this regard, underscoring the significance of the evaluative process of cognition and emotion for adopting certain behavior. This research makes a valuable contribution to the body of knowledge on renewable energy in that the TPB-TAM model has been empirically validated in a Thai household setting. In addition, the findings provide suggestive evidence of a mediating role of attitude in the link between perceived usefulness and intention, although this mediation finding should be interpreted with caution due to the conceptual overlap between these constructs and the absence of bootstrap confidence intervals. Future research is recommended to confirm this mediation pathway using formal bootstrap procedures. Full article

Monitoring the vertical progress of reinforced-concrete buildings supports construction management, urban analytics, and seismic exposure classification, yet camera-based floor counting faces two obstacles: public datasets depict almost exclusively completed structures, and the number of structurally finished floors is visually ambiguous while a building is still being erected. We reformulate building-height estimation as discrete floor-count classification from a single street-level facade image and assemble a 29,049-image multi-source corpus centered on the reinforced-concrete urban stock of Kazakhstan, including a 12-month, fixed-viewpoint sequence of 2255 frames that isolates invariance to construction stage, illumination, weather, and season. We formalize a reproducible annotation protocol for three recurring structural ambiguities—incomplete upper floors, rooftop superstructures, and open ground-level pilotis—and propose DINOv2-MSTS, a dual-branch architecture that aggregates multi-scale patch-token statistics from a frozen self-supervised backbone, trained with an Ordinal-Aware Annotation-Uncertainty (OAU) loss for which its Gaussian spread is learned rather than fixed. On the 5359-image Korter + Mendeley 21-category benchmark, the model attains 80% top-1 accuracy, 94% within $\pm 1$ floor accuracy, and 0.28-floor mean absolute error on this saturated 21-category task (a lower bound for buildings of 21 or more floors) using only 1.84 M trainable parameters, 165× fewer than a fully fine-tuned Vision Transformer, which it outperforms by eight accuracy points. On the separate 2255-frame IITU fixed-label robustness probe, it preserves the correct six-floor prediction in 91% of frames (0.09-floor MAE). The corpus, protocol, architecture, and loss together provide a reproducible benchmark for construction-stage building monitoring. Full article

Solar energy has become one of the most important renewable energy sources for reducing dependence on conventional fossil-based energy systems. Rooftop photovoltaic (PV) installations play a key role in the expansion of solar energy, particularly in tropical countries such as Vietnam. This study experimentally investigates the effects of roof material, rear ventilation gap, PV technology, solar irradiance, and wind speed on the power conversion efficiency (PCE) of rooftop PV modules under tropical climatic conditions in Ho Chi Minh City, Vietnam. Three roof types (concrete, tiled, and corrugated metal), three rear ventilation gaps (10, 30, and 50 cm), and two PV technologies (monocrystalline and polycrystalline) were evaluated under real operating conditions. The results indicate that increased module temperature significantly reduces power output and PCE, even under high solar irradiance. PV modules installed on corrugated metal roofs exhibited the highest operating temperatures and the lowest efficiencies, whereas concrete and tiled roofs provided more favorable thermal conditions. Increasing the rear ventilation gap enhanced convective cooling, with the 30–50 cm configurations showing superior heat dissipation compared with the 10 cm configuration, particularly for corrugated metal roofs. The experimentally determined heat transfer coefficient ranged from 23.48 to 67.64 W m⁻² K⁻¹, exceeding the theoretical wind-based coefficient (16.86–17.22 W m⁻² K⁻¹), thereby indicating the contribution of mixed convection, radiative exchange, and roof–module thermal interactions. Monocrystalline modules consistently achieved slightly higher efficiencies than polycrystalline modules. The findings provide practical guidance for optimizing rooftop PV installations and improving energy yield in tropical climates. Full article

The wind field in urban areas is characterized by an inherent spatial variability, which is also termed spatial uncertainty. This may be manifested as a noticeable difference between rooftop-level measurements in adjacent locations, the degree of which changes throughout the day. In meteorological and environmental contexts, such uncertainty is often described as a probability distribution. Usually, studies deal with the uncertainty of each wind vector component separately, i.e., wind speed and direction. The uncertainty is assumed to be distributed symmetrically around the mean and represented by a single characteristic value. Such representation neglects the correlation between the two wind vector components together. This, in turn, may result in wind vector component combinations that are physically inconsistent with realistic wind regimes. This study proposes a method that quantifies the spatial uncertainty of the urban rooftop wind. It is based on a covariance matrix that quantifies the relationship between the rooftop spatial wind components alongside the seasonal Mahalanobis distance functions. It draws on a representative sample of weather stations and previously calculated seasonal log-logistic Mahalanobis distance functions. Thus, an elliptic-shaped tolerance region is calculated to quantitatively estimate a given proportion of the possible values of the wind vectors at a given time. The model was demonstrated on the metropolitan area of Tel Aviv. The results show that the spatial wind distribution can be very well represented by a small sample of merely four stations. The model’s results were found to be well within the confidence interval, leading to the conclusion that the model is fully capable of providing an accurate description of the current state of the urban wind field. Full article

Designers, engineers, and building officials face increasing pressure to accelerate and improve the accuracy of design review for buildings and infrastructure. Roof assemblies and rooftop structures are particularly challenging due to the complexity and fragmentation of regulatory requirements, especially in jurisdictions such as Florida, where compliance must be verified across both the residential and commercial volumes of the Florida Building Code (FBC). The resulting review process is technically demanding and time-intensive, imposing significant cognitive and operational burdens on practitioners and under-resourced public agencies. To address these challenges, this study proposes and evaluates an agentic artificial intelligence (AI) framework for automated code compliance checking of roof assemblies and rooftop structures. The framework employs a multi-agent architecture in which specialized AI agents collaboratively interpret regulatory provisions and evaluate roof design parameters across four core modules: data preprocessing and code ingestion, rule-based and semantic analysis, results visualization, and iterative validation. YOLO11m-seg and Mask R-CNN were used for element detection and segmentation, and the system was developed using 150 design projects, including roof plans, section details, and specifications. Four large language models from two families (Mistral and GPT) were comparatively evaluated on standardized compliance tasks. The framework was then tested on a held-out portfolio of 15 distinct roof-design projects comprising 60 code-compliance decisions derived from the FBC 2023, with performance measured by precision, recall, F1-score, and accuracy. GPT-5.4 achieved the highest overall performance (F1 = 0.97; accuracy = 97%). Because the reasoning and vision components were evaluated separately rather than as an integrated end-to-end pipeline, and the scope was limited to one jurisdiction and two drawing types, broader code coverage and production-setting validation are needed before claims of generality. Nonetheless, the results suggest that agentic AI can meaningfully support compliance review and reduce reviewer burden in roof-design permitting. Full article

Therapeutic gardens are increasingly integrated into healthcare planning and design, supported by evidence showing that exposure to nature promotes health and well-being. As urbanisation and densification intensify, rooftop gardens offer a sustainable means of providing health-promoting green spaces in urban settings. This study aimed to deepen understanding of the EBD process behind a purpose-built rooftop garden at an urban Memory Clinic. It examined how the garden was experienced in terms of perceived successes and shortcomings and which design decisions or contextual factors were most influential. A POE was conducted through focus group interviews with healthcare professionals and an interview with the responsible landscape architect. Data were analysed using thematic analysis, producing five themes organised around three questions: How the garden turned out, why it turned out that way, and IF changes were desirable. Findings show that while the garden exceeded expectations regarding aesthetics, restorative qualities, and staff use, it was not used for patient-oriented therapeutic activities as intended. This divergence was linked less to physical design quality than to organisational change, the loss of key actors, insufficient documentation of design intentions, procurement disruptions, shifting clinical priorities, and maintenance arrangements. The study highlights “implementation drift” as a critical risk in EBD processes. Full article

Smart-city advanced metering infrastructure enables utility-scale remote analytics, but some forms of under-registration closely resemble lawful changes in demand and are hard to model as anomalies. We study a narrow, physically motivated event family at the single-meter level, namely multiplicative under-registration with unknown onset (a shunt-like attack), in which recorded active energy is approximately scaled by a factor $\alpha <1$ after a change-point while the daily-profile structure and spectral shape remain invariant. We formalize the problem and develop a physics-guided detector family based on weighted daily-profile regression (GLS) and its robust variant (RGLS), with quality-control filters, spectral-consistency checks, and an optional reactive-channel gate, designed to stay selective under confounders such as rooftop photovoltaics, electric-vehicle charging, and heat-pump onsets. On a device-disjoint Low Carbon London benchmark (487 households) the preferred GLS detector attains precision $0.915$, recall $0.978$, and $F_1=0.945$ at $\alpha =0.10$ while keeping the non-theft suspected rate near $1\%$; a cross-dataset check on Open Power System Data with real EV/PV/heat-pump overlays yields zero false alarms on all 72 cases, and Mendeley and WPuQ benchmarks add a second large family and a reactive-channel test. We compare against external baselines (classical change-point detection, Isolation Forest, autoencoder, LSTM, gradient boosting, and a supervised statistical pipeline) on the same protocol: generic anomaly detectors fail on this shape-preserving attack, and supervised models match the detector only in-distribution while, unlike it, failing to transfer to real lawful confounders. All metrics carry bootstrap confidence intervals, and a full reproducibility bundle accompanies the submission. Full article

To address the rockburst hazard caused by overhanging hard roofs and difficult caving during the initial mining period of thick coal seam fully mechanized working faces, this study takes the N4202 fully mechanized top coal caving working face of the Santunzi Coal Mine as the field engineering background. The mined No. 4-1 coal seam has an average thickness of 9.46 m, and its overlying hard roof is composed of medium sandstone and siltstone. A total of 39 hydraulic fracturing boreholes, including type A, type B, type C1/C2, and fan-shaped holes, were deployed, with a designed fracturing depth of 19 m. Three testing means, including a CXK12(B) borehole imaging instrument, a KJ1222 microseismic monitoring system, and on-site roof caving observations, were adopted to comprehensively evaluate the field performance of roof hydraulic fracturing, and the rockburst prevention mechanism was analyzed. The field test results indicate that dense and well-connected fractures are formed after fracturing, with more than 8 fractures per single borehole and a fracture aperture of 0.8–2.2 mm, and the connectivity rate between adjacent fracturing boreholes reaches 92.3%. The initial mining top caving step distance of the working face is reduced to 13.2 m, while the theoretical calculated values are 10 m for the immediate roof and 15.6 m for the main roof. The roof gradually collapses, and the mining pressure is alleviated. During fracturing, the frequency and energy of microseismic events increase by 285% and 230%, respectively, compared to the state before fracturing. In the subsequent mining process, the maximum microseismic energy is only 4.56 kJ, which is far lower than the rockburst critical energy threshold (20 kJ) of this mine. Therefore, no rockburst hazard occurs in the working face. These research findings can provide a practical technical reference for rockburst prevention using hard roof hydraulic fracturing in similar thick coal seam fully mechanized mining faces. Full article

The increasing penetration of photovoltaic (PV) systems requires accurate and stable short-term forecasting to ensure reliable grid operation under operational constraints. This paper investigates short-horizon multi-step PV power forecasting using one full year of high-resolution (5 min) real-world data from a 111-kW grid-connected rooftop installation. The forecasting problem is formulated as a direct multi-output supervised learning task with a 30 min prediction horizon. A comprehensive comparative evaluation is conducted across baseline (persistence), tree-based (XGBoost), and deep learning architectures (LSTM, GRU, and Temporal Convolutional Networks—TCN). Results show that deep learning models significantly outperform conventional baselines, with LSTM achieving the lowest normalized RMSE (≈10.3%), while TCN provides a competitive trade-off between predictive accuracy, temporal stability, and computational efficiency. The direct multi-step formulation was adopted to reduce potential error propagation effects commonly observed in recursive forecasting approaches. Beyond forecasting accuracy, the study evaluates computational complexity and inference latency to assess practical deployability in resource-constrained environments. The proposed framework demonstrates that high-resolution real-world PV forecasting can achieve both strong predictive performance and operational feasibility. These findings contribute to the development of robust short-term forecasting strategies for distributed renewable energy systems operating under regulatory export constraints. Full article

This study investigates the aerodynamic performance of a two-building ensemble as an integrated architectural–aerodynamic system, with a focus on airflow conditions relevant to building-integrated wind turbines. The research addresses the question of whether newly designed development can actively improve, rather than deteriorate, airflow conditions above existing buildings. A parametric CFD analysis based on steady-state RANS (SST k–ω) simulations was conducted for multiple geometric configurations of a reference building (A) and a neighboring building (B), varying roof pitch (22–40°) and height. Airflow was evaluated using mean longitudinal velocity (Vy), coefficient of variation (CV), and vector components across three architectural scenarios corresponding to different turbine-integration strategies. The results demonstrate that properly designed geometries can significantly enhance flow quality. In the near-roof scenario (Arch1), the optimal configuration achieved a 24.28% increase in Vy and a 94.53% reduction in CV, indicating strong flow stabilization. In the façade-integration scenario (Arch2), improvements reached +10.40% in Vy and −23.16% in CV, reflecting vertical homogenization of the flow field. In the point-based scenario (Arch3), a local velocity increase of 4.29% was obtained while maintaining directional stability. The findings indicate that building geometry acts as an active design parameter that controls flow intensity, homogeneity, and direction. The study proposes a CFD-based decision framework and demonstrates that architectural form can be deliberately shaped to enhance wind conditions, supporting the integration of wind turbines into coherent building design. Full article