Search Results (159)

The integration of vertical greenery systems (VGSs) into existing reinforced concrete (RC) buildings raises questions regarding interface kinematics and the permanent displacement of soil-retaining elements under seismic excitation. This study experimentally investigates the residual displacement of façade-mounted living walls and rooftop planter pods anchored to a deficient RC frame under shake table excitation. A 1:3 scale reinforced concrete frame was tested in two distinct phases: initially as a deficient, unretrofitted structure (Phase A), and subsequently as a retrofitted system integrated with vertical greenery elements (Phase B). High-precision terrestrial laser scanning (TLS) was employed before and after successive seismic excitation stages to generate dense three-dimensional point clouds. Cloud-to-cloud comparison techniques were used to quantify global structural displacement and local kinematic behavior of greenery components, while results were validated against conventional displacement sensors. The RC frame exhibited millimeter-scale permanent displacements consistent with draw-wire measurements. In contrast, planter pods demonstrated configuration-dependent behavior, including up to 8 cm translational sliding and rotational responses reaching 13° under repeated excitation, whereas living wall panels remained stable. Notably, a 95% reduction in point cloud density reproduced global deformation patterns with an RMSE of 3.03 mm and quantified peak displacements with only ~2% deviation from full-resolution results. The findings demonstrate the capability of TLS-based monitoring to detect differential kinematic behavior of integrated VGSs, while highlighting the variability in performance of friction-based rooftop anchorage utilizing different robust planter pod fixing systems. Full article

This study presents a performance and techno-economic evaluation of a 24 kWp grid-connected rooftop photovoltaic system in Yeşilova, Burdur, Türkiye, based on measured operational data from 2024. Beyond conventional software comparisons, this research establishes a validated benchmarking protocol for medium-scale rooftop PV systems by quantifying the divergence between measured data and predictive modeling under fluctuating seasonal conditions. Measured results were compared with energy yield predictions from PVsyst and HelioScope. Key performance indicators, including final yield, performance ratio (PR), and capacity factor, were evaluated alongside main loss components. The system produced an annual energy output of 33,977.5 kWh, corresponding to an average PR of 75.7% and a capacity factor of 16.99%. Simulation results show deviations from measured values, with PVsyst moderately overestimating and HelioScope underestimating the annual yield. Thermal effects were identified as the dominant contributor to performance losses, particularly during elevated summer temperatures. A techno-economic assessment indicates a payback period of 8.4 years, a levelized cost of electricity (LCOE) of 0.0485 US$/kWh, and an internal rate of return (IRR) of 15.58%. These findings underline the importance of validating simulation-based assessments with site-specific measurements to improve the reliability of photovoltaic system performance and investment evaluations. Full article

Accurate time-domain photovoltaic (PV) models are needed to evaluate performance under outdoor variability beyond STC datasheet conditions. This paper presents a traceable modeling workflow based on the standard single-diode formulation, implemented in MATLAB/Simulink (R2023a) as a modular white-box architecture that explicitly resolves photocurrent generation and loss mechanisms (diode recombination, shunt leakage, and series resistance effects) with temperature-consistent propagation through $V_T\left(T\right)$ and saturation-current terms. The method couples optical boundary conditions to the electrical model by embedding plane-of-array (POA) excitation via the incidence angle $\theta \left(t\right)$ and roof albedo directly into the photocurrent source term, preserving the causal chain from mounting geometry to electrical response. Calibration is separated from prediction by initializing key parameters using the standard Simulink PV block and then freezing them for time-domain evaluation. The workflow is validated on a 395 W rooftop prototype using 1 min resolved POA irradiance (ISO 9060:2018 Class A radiometric chain) and module temperature (IEC 60751 Class A Pt100), synchronized with electrical measurements. Over a multi-week campaign, the model exhibits high fidelity, with a worst-case relative current error of ~1.1% and a consistently low bias and dispersion, quantified by ME, MAE, RMSE, ${\sigma }_e$, and thresholded MAPE. Full article

In GNSS-denied large-scale outdoor environments, UAVs and UGVs that rely solely on visual–inertial odometry (VIO) suffer from accumulated global drift as the trajectory grows. Meanwhile, inter-platform ultra-wideband (UWB) ranging exhibits unknown, time-varying noise under NLOS/multipath, rendering naïve weighting unreliable. This paper presents an anchor-free collaborative localization framework for UAV–UGV teams that fuses pairwise UWB ranges (including UAV–UAV, UAV–UGV, and UGV–UGV) with onboard VIO in a factor-graph backend via a two-stage robust scheme. First, we bound VIO drift using per-agent state covariance and reject UWB outliers with a Mahalanobis gate, preventing early-stage bias when VIO is still accurate. Then, during global optimization, we adaptively estimate the Fisher information of UWB factors from measurement–state residuals, enabling online self-tuning of measurement confidence under time-varying SNR. Real-world experiments with three UAVs and two UGVs over multi-level rooftops and forest–open areas (~1.6 km²) show that, compared to an outlier-only variant, the proposed method further reduces localization RMSE by about 24.6% and maximum error by about 31.2% for both UAVs and UGVs, maintaining strong performance during long trajectories dominated by VIO drift and NLOS ranges. The approach requires no fixed anchors or GNSS and is applicable to UAV–UGV teams for disaster response, cooperative mapping/inspection, and bandwidth-limited operations. Full article

This study investigates the characterisation of particulate matter (PM) across different size fractions (TSP, PM₁₀, PM₄, PM_2.5, and PM₁) in Porto, Portugal, over a 2-year period. Sampling was conducted at two heights (ground level and rooftop), integrating real-time measurements and filter-based analyses to evaluate seasonal and spatial variations. Elemental composition was determined using Inductively Coupled Plasma–Mass Spectrometry (ICP-MS), enabling detailed assessments of 30 chemical elements. Meteorological parameters, including temperature, precipitation, wind speed, and direction, were analysed to understand their influence on PM concentrations. Results indicate that significant seasonal trends, with higher PM concentrations observed during autumn and winter, were associated with low boundary layer height, promoting greater mixing of particles, enhanced deposition, and higher anthropogenic emissions, with average seasonal TSP values ranging from 0.001 to 0.059 µg m⁻³. Elemental analysis revealed distinct profiles at ground and rooftop levels, with Ba, Cu, Pb, Mg, and Na among the most frequently detected elements; ground-level samples showed stronger contributions from local sources, such as traffic, while rooftop samples reflected regional and long-range transport. Meteorological factors, such as precipitation and wind speed, exhibited negative correlations with PM concentrations, underscoring their role in atmospheric washing. These findings highlight the complex interplay of local and regional factors in shaping PM dynamics and emphasise the importance of multi-level monitoring for effective air-quality management. Full article

As urbanization expands, the loss of pervious surfaces has led to greater stormwater runoff and contributed to an increase in urban flooding—localized flooding in areas not formally designated as flood zones. This study evaluates the potential of decentralized active rainwater harvesting (RWH) to mitigate urban flooding in semi-arid urban environments. A neighborhood in northeast El Paso, Texas, was selected as a pilot site. Using a GIS-HEC-HMS modeling framework, approximately 9000 residential parcels were analyzed to assess rooftop harvesting capacity, runoff potential, and system feasibility under different adoption rates and antecedent moisture conditions. Land cover and building footprints were extracted using supervised machine learning to generate stormwater runoff parameters and catchment areas for rainfall-runoff simulations for storms with return periods ranging from 1 to 50 years. The results indicate that for 1- and 2-year storms, a 25% adoption rate may reduce street runoff by 16–19% from 13.1 to 10.6 × 10³ m³ and from 31 to 26.1 × 10³ m³. Increasing adoption to 50% yields substantially greater reductions of approximately 30–36%. Even higher-magnitude storms (5- and 10-year events) experience measurable decreases in runoff volume, with reductions of 10% for the 5-year storms and up to 10.4% for the 10-year storm at the 25% adoption and 20–22% across the same events at 50% adoption. Overall, the results of this study demonstrate that GIS and HEC-HMS are effective tools for evaluating urban flood mitigation strategies, and that decentralized RWH offers a viable method for reducing flood risk in urbanized settings when adoption levels and storage capacities are considered. Full article

This study examines the impact of updated weather datasets on the selection of energy-efficient measures (EEMs) and photovoltaic (PV) sizing to reach net-zero energy (NZE) performance for residential buildings in four USA cities with different climates: Minneapolis, MN, Chicago, IL, San Francisco, CA, and Phoenix, AZ. The analysis results show that annual heating and cooling degree days from various climatic datasets can differ from those estimated using 2025 data by 0.5–23.3%. These differences in climatic datasets can result in substantial deviations in predicting annual energy needs for residential buildings. For buildings in heating-dominated climates, such as Minneapolis, MN, significant reductions in annual energy demands of up to 8.2% are incurred when using the most recent climate dataset, while increases of up to 5.0% are found for the cooling-dominated climate of Phoenix, AZ. Moreover, variations in climatic datasets affect design specifications and economic indicators of residential buildings. Life-cycle costs of baseline designs are lowered by 3.6% for buildings in Minneapolis, MN, but are increased by 2.3% for buildings in Phoenix, AZ. Similarly, capacities for rooftop PV systems required for net-zero energy designs decrease by 9.5% for buildings in Minneapolis and increase by 11.7% for buildings in Phoenix, AZ. Full article

This study investigates direction-dependent roof speed-up factors for both isolated and interacting building configurations and evaluates their influence on the energy-yield potential of small wind turbines (SWTs) in urban environments. A combined approach was adopted: A theoretical framework and wind-tunnel experiments were developed to establish a general understanding of the meteorological, aerodynamic, and energetic parameters governing rooftop wind energy conversion and to derive characteristic roof speed-up factors for standardized flat-roof configurations. Wind-tunnel experiments were conducted for four distinct building scenarios, differing in layout and surrounding interaction, under three representative wind directions. High-resolution velocity measurements were acquired at multiple rooftop positions and elevations to capture detailed flow-acceleration and turbulence patterns. The resulting data were then applied in a case study for a representative urban site in Aachen, Germany. The measured directional speed-up factors were combined with a Weibull wind-speed distribution and a representative SWT power curve to estimate annual energy yields. The results reveal pronounced spatial and directional variability in wind acceleration, with localized increases of up to 25%. These variations translate into substantial differences in expected turbine performance depending on mounting height, placement, and prevailing wind direction. To facilitate further research and practical use, the complete dataset is published openly as a benchmark for computational fluid dynamics (CFD) validation and as a planning resource for rooftop turbine siting. The study underscores the importance of local aerodynamic effects in urban wind-energy design and provides a methodological framework that links controlled wind-tunnel data with real-world wind statistics. Full article

This study quantitatively evaluates the health impacts of urban temperature changes and the potential health benefits of highly reflective roofs as an urban heat island (UHI) mitigation measure. First, empirically derived relationships between ambient temperature and health-related indicators were established for multiple diseases, including both fatal and non-fatal outcomes. Health impacts were assessed using disability-adjusted life years (DALYs), integrating years of life lost (YLLs) and years lived with disability (YLDs). Target diseases included heat- and cold-related mortality, heatstroke, infectious diseases, sleep disturbance, and fatigue. Next, a meteorological simulation was conducted using a Weather Research and Forecasting (WRF) model to estimate outdoor air temperature changes resulting from the implementation of highly reflective building roofs in Osaka Prefecture, Japan. Roof surface reflectance was increased from 0.15 to 0.65 within an urban canopy model, and temperature reductions were evaluated at a 2 km spatial resolution for a one-year period. The results indicate that highly reflective roofs reduced daytime air temperatures by approximately 1.2–1.8 °C, with greater effects observed in high-density urban areas. By integrating the simulated temperature reductions with the temperature–health relationships, annual health impacts were quantified. Although wintertime increases in cold-related health burdens were observed, the annual cumulative DALYs decreased by 1767, corresponding to approximately a 5% reduction in total temperature-related health burdens in Osaka Prefecture. These findings demonstrate that rooftop reflectivity enhancement can contribute to net health improvements while highlighting the importance of accounting for seasonal trade-offs in UHI mitigation strategies. Full article

Sustainable management of indoor and outdoor air quality is essential for protecting public health, enhancing well-being, and supporting resilient urban environments. Low-cost air quality sensors enable continuous, real-time monitoring of key pollutants and, when combined with data analytics, provide scalable and cost-effective insights for smart building operation and environmental decision-making. This pilot study evaluates an indoor–outdoor air quality monitoring system deployed at the Faculty of Electrical Engineering and Information Technologies in Skopje, with a focus on: (i) PM2.5 and PM10 concentrations and their relationship with meteorological conditions and human occupancy; (ii) sensor responsiveness and reliability in an educational setting; and (iii) implications for sustainable building operation. From January to March 2025, two indoor sensors (a classroom and a faculty hall) and two outdoor rooftop sensors continuously measured PM2.5 and PM10 at one-minute intervals. All sensors were calibrated against a reference instrument prior to deployment, while meteorological data were obtained from a nearby station. Time-series analysis, Pearson correlation, and multiple regression were applied. Indoor particulate levels varied strongly with occupancy and ventilation status, whereas outdoor concentrations showed weak to moderate correlations with meteorological variables, particularly atmospheric pressure. Moderate correlations between indoor and outdoor PM suggest partial pollutant infiltration. Overall, this pilot study demonstrates the feasibility of low-cost sensors for long-term monitoring in educational buildings and highlights the need for adaptive, context-aware ventilation strategies to reduce indoor exposure. Full article

Radiative cooling (RC) offers a passive pathway to reduce surface and system temperatures by emitting thermal radiation through the atmospheric window, yet its daytime effectiveness is often constrained by geometry, angular solar exposure, and practical integration limits. This work experimentally investigates the use of passive non-imaging optics, specifically compound parabolic concentrators (CPCs), as enhancers of RC performance under realistic conditions. A three-tier experimental methodology is followed. First, controlled indoor screening using an infrared lamp quantifies the intrinsic heat gain suppression of a commercial RC film, showing a temperature reduction of nearly 88 °C relative to a black-painted reference. Second, outdoor rooftop experiments on aluminum plates assess partial RC coverage, with and without CPCs, under varying orientations and tilt angles, revealing peak daytime temperature reductions close to 8 °C when CPCs are integrated. Third, system-level validation is conducted using a modified GUNT ET-202 solar thermal unit to evaluate the transfer of RC effects to a water circuit absorber. While RC strips alone produce modest reductions in water temperature, the addition of CPC optics amplifies the effect by factors of approximately three for ambient water and nine for water at 70 °C. Across all configurations, statistical analysis confirms stable, repeatable measurements. These results demonstrate that coupling commercially available RC materials with non-imaging optics provides consistent and measurable performance gains, supporting CPC-assisted RC as a scalable and retrofit-friendly strategy for urban and building energy applications while calling for longer-term experiments, durability assessments, and techno-economic analysis before deriving definitive deployment guidelines. Full article

Accurate forecasting of photovoltaic (PV) power generation is essential for mitigating weather-induced variability and maintaining power-system stability. This study aims to improve PV power forecasting accuracy by enhancing the quality of numerical weather prediction (NWP) inputs rather than modifying forecasting model structures. Specifically, systematic errors in temperature, wind speed, and solar radiation data produced by the Unified Model–Local Data Assimilation and Prediction System (UM-LDAPS) are corrected using a Model Output Statistics (MOS) approach. A case study was conducted for a 20 kW rooftop PV system in Buan, South Korea, comparing forecasting performance before and after MOS application using a random forest-based PV forecasting model. The results show that MOS significantly improves meteorological input accuracy, reducing the root mean square error (RMSE) of temperature, wind speed, and solar radiation by 38.1–62.3%. Consequently, PV power forecasting errors were reduced by 70.0–78.7% across lead times of 1–6 h, 7–12 h, and 19–24 h. After MOS correction, the normalized mean absolute percentage error (nMAPE) remained consistently low at approximately 7–8%, indicating improved forecasting robustness across the evaluated lead-time ranges. In addition, an economic evaluation based on the Korean renewable energy forecast-settlement mechanism estimated an annual benefit of approximately 854 USD for the analyzed 20 kW PV system. A complementary valuation using an NREL-based framework yielded an annual benefit of approximately 296 USD. These results demonstrate that improving meteorological data quality through MOS enhances PV forecasting performance and provide measurable economic value. Full article

The increasing penetration of electric vehicles (EVs) and rooftop photovoltaics (PV) is intensifying the variability and uncertainty of residential net demand, thereby challenging real-time operation in smart grids and microgrids. The purpose of this study is to develop and evaluate an accurate and operationally relevant short-term forecasting framework that jointly models household net demand and EV charging behaviour. To this end, a Residual-Normalised Multi-Task GRU (RN-MTGRU) architecture is proposed, enabling the simultaneous learning of shared temporal patterns across interdependent energy streams while maintaining robustness under highly non-stationary conditions. Using one-minute resolution measurements of household demand, PV generation, EV charging activity, and weather variables, the proposed model consistently outperforms benchmark forecasting approaches across 1–30 min horizons, with the largest performance gains observed during periods of rapid load variation. Beyond predictive accuracy, the relevance of the proposed approach is demonstrated through a demand response case study, where forecast-informed control leads to substantial reductions in daily peak demand on critical days and a measurable annual increase in PV self-consumption. These results highlight the practical significance of the RN-MTGRU as a scalable forecasting solution that enhances local flexibility, supports renewable integration, and strengthens real-time decision-making in residential smart grid environments. Full article

This study establishes a multi-scale validation framework for Computational Fluid Dynamics (CFD) simulations of building-induced pollutant dispersion, integrating wind tunnel experiments, the CEDVAL benchmark dataset, and field measurements from a thermal power plant that serves as a proxy for nuclear facilities. The RNG k-ε and Large Eddy Simulation (LES) models were evaluated across these validation tiers. Results demonstrate that both models effectively capture key flow characteristics, with LES showing superior performance in predicting roof-level velocity and turbulence intensities. A systematic overestimation of rooftop and leeward concentrations was observed, though predictive accuracy improved with downwind distance (e.g., FAC2 > 0.5). The RNG k-ε model provided the best balance between accuracy and computational efficiency for engineering applications, while LES is recommended for high-fidelity near-field analysis. This work provides validated methodologies for environmental risk assessment in nuclear power planning and emission control strategy development. Full article

Wind is one of the main environmental loads acting on temporary scaffolding structures, yet current design codes apply simplified assumptions regarding its distribution. This study presents full-scale measurements of wind velocities on 10 façade scaffolds located across Poland, representing various building geometries and exposure conditions. Each scaffold was instrumented with five two-dimensional ultrasonic anemometers and one three-dimensional rooftop reference anemometer. Data were analysed in 10 min averages, divided into 30° directional sectors and compared with the normative model defined in EN 12811-1 using the site factor $c_s$. The results reveal strong spatial variability of wind action across scaffold surfaces, with measured local velocities ranging from 20% to 140% of the reference values. The parallel flow component exhibited substantial scatter, while the perpendicular component was strongly damped by façade shielding and protective netting. For most mid-façade positions, measured values corresponded to $c_s=0.25–0.5$, whereas corner and edge locations frequently exceeded $c_s=1.0$. The findings demonstrate that the uniform site factors assumed in current standards do not capture the aerodynamic complexity of real scaffolds, especially under oblique or high-intensity wind conditions. The presented dataset provides a unique experimental basis for improving scaffold wind load modelling and developing position-specific design provisions. Full article