Search Results (218)

Photovoltaic/Thermal (PV/T) technology efficiently harnesses solar energy by co-generating electricity and hot water. Unlike conventional PV systems, PV/T systems improve thermal utilization, cool PV modules, and prevent performance degradation caused by high temperatures. Among the various PV/T configurations, micro-channel heat pipe (MCHP) systems are prominent due to their ability to enhance heat transfer through the use of vacuum-filled, refrigerant-sealed MCHPs. This study explores how factors such as working fluid type, evaporation section heat flux, fill ratio, and condensation section length impact system performance. A 3D steady-state CFD model simulating phase-change heat transfer was developed to analyze thermal and electrical efficiencies. The results reveal that R134a outperforms acetone in heat transfer, with thermal resistance showing a significant decrease (from 0.5 °C·W⁻¹ at a 30% fill rate to 0.3 °C·W⁻¹ at a 70% fill rate) under varying heat source powers. The optimal fill ratio depends on the heat flux; for powers up to 70 W, the fill ratio ranges from 30% to 50%, while above 70 W, it shifts to 60–80%. Additionally, a longer condensation section reduces thermal resistance by up to 30% and enhances heat transfer efficiency, improving the overall system performance by 10%. These findings offer valuable insights into optimizing MCHP PV/T systems for increased efficiency. Full article

Nowadays, bifacial photovoltaic (PV) technology has emerged as a key solution to enhance the energy yield of large-scale PV plants, especially when integrated with sun-tracking systems. This study investigates the quantification of bifaciality productivity for two multi-MW PV plants in southern Italy (Sicily) equipped with monocrystalline silicon bifacial modules installed on single-axis east–west tracking systems and aligned in the north–south direction. An optimized energy model was developed at the stringbox level, employing a dedicated procedure including data filtering, clear-sky condition selection, and numerical estimation of bifaciality factors. The model was calibrated using on-field measurements acquired during the first operational months to minimize uncertainties related to degradation phenomena. The application of the model demonstrated that the rear-side contribution to the total energy output is non-negligible, resulting in additional energy gains of approximately 5.3% and 3% for the two plants, respectively. Full article

China’s urbanisation has transitioned from an era of rapid, coarse expansion to one of refined and targeted development. In accordance with China’s “dual-carbon” strategy, the building sector—presently the third-largest source of domestic carbon emissions—is compelled to pursue emission optimisation in its forthcoming evolution. Photovoltaic-building technologies offer an effective response to this imperative. Within the context of accelerating high-rise residential construction, the architectural integration of scientifically configured photovoltaic façades has emerged as a critical challenge. Employing an integrated methodology of urban surveying and simulation, this study examines the façade characteristics of residential buildings in northern Chinese cities, selecting Xi’an as the representative case. Three PV-facade integration strategies for existing stock are presented: window retrofitting, wall retrofitting, and full-façade renovation. Utilising the EnergyPlus platform, the manuscript simulates the electrical demand profiles and clean-electricity generation of typical dwellings under varying photovoltaic materials and configuration schemes, while concurrently assessing economic performance. It demonstrates that a judicious determination of photovoltaic installation scale and layout strategy markedly amplifies energy-saving efficacy, diminishes aggregate energy consumption and carbon emissions, and simultaneously reduces the capital expenditure of photovoltaic systems. For multi-story buildings, a full façade retrofit yielded the highest annual electricity generation of 514,703.56 kWh and an annual carbon reduction of 15,521.50 kgCO₂. For high-rise buildings, installing PV modules only above the 20th floor increased the effective generation ratio from 45.24% to 87.17%, while the carbon reduction efficiency per unit investment improved from 0.05 to 0.22 kgCO₂/¥. Full article

The optimal utilization of UAV-integrated photovoltaic (PV) systems demands accurate modeling that accounts for dynamic flight conditions. This paper introduces a novel computational intelligence-based framework that models the behavior of a moving PV system mounted on a UAV. A unique mathematical approach is developed to translate UAV flight dynamics, specifically roll, pitch, and yaw, into the tilt and azimuth angles of the PV module. To adaptively estimate the diode ideality factor under varying conditions, the Grey Wolf Optimization (GWO) algorithm is employed, outperforming traditional methods like Particle Swarm Optimization (PSO). Using a one-year environmental dataset, multiple machine learning (ML) models are trained to predict maximum power point (MPP) parameters for a commercial PV panel. The best-performing model, Rational Quadratic Gaussian Process Regression (RQGPR), demonstrates high accuracy and low computational cost. Furthermore, the proposed ML-based model is experimentally integrated into an incremental conductance (IC) MPPT technique, forming a hybrid MPPT controller. Hardware and experimental validations confirm the model’s effectiveness in real-time MPP prediction and tracking, highlighting its potential for enhancing UAV endurance and energy efficiency. Full article

It is well known that temperature strongly affects the photovoltaic (PV) performance. Raising the working temperature leads to a significant decrease in PV output of the power capacity, and it also lowers power conversion efficiency. This issue is highly important for the PV systems operating in tropical climate areas such as southern Viet Nam. Developing the cooling methods applied for reducing the PV module temperature might be the solution to this problem and has attracted many researchers and industrial sectors. However, the existing research might not sufficiently address the comparative evaluation of multiple active water-based cooling methods on power conservation efficiency, power output, and cost implications under high-temperature conditions in tropical areas. This study is a case study that aims at conducting some experimental investigations for active water-based cooling methods applied to PV modules in Ho Chi Minh City, South Viet Nam. There are four active water-based cooling methods, including the spraying liquid method (SL), the dripping droplet method (DD), tube heat exchanger method (TE), and the liquid flowing on the PV surface method (LF), that have been developed and experimentally investigated. The voltage, current, temperature, and humidity of the PV cells have been automatically recorded in every one-minute interval via sensors and electronic devices. The experimental results indicate that the surface temperature, the power conversion efficiency, and the output power of PV module are developed toward the useful and positive direction with four cooling methods. In detail, the SL is the best one, in which it leads the PV temperature to reduce from 52 °C to 34–35 °C, the output power increases up to 6.3%, its power conversion efficiency improves up to 2%, while the water flow rate is at its lowest with 0.65 L/min. Similarly, LF also creates results that are similar to SL, but it needs a higher amount of cooling water, which is up to 3.27 L/min. The other methods, like DD and TE, have less power conversion efficiency compared to the SL; it improves only around 1 to 1.3%. These results might be useful for improving the benefits of PV power generation in some tropical regions and contributing to the green energy development in the world. Full article

Perovskite solar cells (PSCs) rarely report, on a single-device platform, concurrent gains in optoelectronic efficiency and buried-interface mechanical robustness—two prerequisites for flexible and roll-to-roll (R2R) integration. We engineered a nanoplate-structured fluorine-doped tin oxide (NP-FTO) front electrode that couples light management with three-dimensional interfacial anchoring, and we quantified both photovoltaic (PV) and nanomechanical metrics on the same device stack. Relative to planar FTO, the NP-FTO PSCs achieved PCE of up to 25.65%, with simultaneous improvements in Voc (to 1.196 V), Jsc (up to 26.35 mA cm⁻²), and FF (to 82.65%). Nanoindentation revealed a ~28% increase in reduced modulus and >70% higher hardness, accompanied by a ~32% reduction in maximum indentation depth, indicating enhanced load-bearing capacity consistent with the observed FF gains. The low-temperature, solution-compatible NP-FTO interface is amenable to R2R manufacturing and flexible substrates, offering a unified route to bridge high PCE with reinforced interfacial mechanics toward integration-ready perovskite modules. Full article

Photovoltaic (PV) systems are increasingly integral to global energy solutions, but their long-term reliability is challenged by various operational faults. In this article, we propose an advanced hybrid diagnostic framework combining a Deep Belief Network (DBN) for feature pattern extraction and a Light Gradient Boosting Machine (LightGBM) for classification to detect and diagnose PV panel faults. The proposed model is trained and validated on the QASP PV Fault Detection Dataset, a real-time SCADA-based dataset collected from 255 W panels at the Quaid-e-Azam Solar 100 MW Power Plant (QASP), Pakistan’s largest solar facility. The dataset encompasses seven classes: Healthy, Open Circuit, Photovoltaic Ground (PVG), Partial Shading, Busbar, Soiling, and Hotspot Faults. The DBN captures complex non-linear relationships in SCADA parameters such as DC voltage, DC current, irradiance, inverter power, module temperature, and performance ratio, while LightGBM ensures high accuracy in classifying fault types. The proposed model is trained and evaluated on a real-world SCADA-based dataset comprising 139,295 samples, with a 70:30 split for training and testing, ensuring robust generalization across diverse PV fault conditions. Experimental results demonstrate the robustness and generalization capabilities of the proposed hybrid (DBN–LightGBM) model, outperforming conventional machine learning methods and showing an accuracy of 98.21% classification accuracy, 98.0% macro-F1 score, and significantly reduced training time compared to Transformer and CNN-LSTM baselines. This study contributes to a reliable and scalable AI-driven solution for real-time PV fault monitoring, offering practical implications for large-scale solar plant maintenance and operational efficiency. Full article

This study presents a novel framework to assess the combined impact of soiling and thermal effects on rooftop PV systems through multi-seasonal, multi-site field campaigns in an industrial-urban environment. This work addresses key research gaps by providing a high-resolution, site-specific analysis that captures the synergistic effect of particulate accumulation and thermal stress on PV performance in an industrial-urban environment—a setting distinct from the well-studied arid climates. The study further bridges a gap by employing controlled pre- and post-cleaning performance tests across multiple sites to isolate and quantify soiling losses, offering insights crucial for developing targeted maintenance strategies in pollution-prone urban areas. Unlike previous work, it integrates gravimetric soiling measurements with high-resolution electrical (I–V), thermal, and environmental monitoring, complemented by PVSYST simulation benchmarking. Field data were collected from five rooftop plants in Bursa, Türkiye, during summer and winter, capturing seasonal variations in particulate deposition, module temperature, and PV output, alongside irradiance, wind speed, and airborne particulates. Soiling nearly doubled in winter (0.098 g/m²) compared to summer (0.051 g/m²), but lower winter temperatures (mean 19.8 °C) partially offset performance losses seen under hot summer conditions (mean 42.1 °C). Isc correlated negatively with both soiling (r = −0.68) and temperature (r = −0.72), with regression analysis showing soiling as the dominant factor (R² = 0.71). Energy yield analysis revealed that high summer irradiance did not always increase output due to thermal losses, while winter often yielded comparable or higher energy. Soiling-induced losses ranged 5–17%, with SPP-2 worst affected in winter, and seasonal PR declines averaged 10.8%. The results highlight the need for integrated strategies combining cleaning, thermal management, and environmental monitoring to maintain PV efficiency in particulate-prone regions, offering practical guidance for operators and supporting renewable energy goals in challenging environments. Full article

One of the most important applications of solar energy is electricity generation using photovoltaic (PV) panels. Yet, as the temperature of PV modules rises, both their efficiency and service life decline. A common approach to mitigate this issue is cooling with fins, a design that is now widely adopted. However, traditional fin-based cooling systems often fail to deliver adequate performance in hot regions with strong solar radiation. In particular, passive cooling alone shows limited effectiveness under conditions of high ambient temperatures and intense sunlight, such as those typical in Malaysia. To address this limitation, hybrid cooling strategies, especially those integrating both air and water, have emerged as promising solutions for enhancing PV performance. In this study, an experimental and economic investigations were carried out on a PV cooling system combining copper tubes and aluminium fins, tested under Malaysian climatic conditions. The economic feasibility was evaluated using the Simple Payback Period (SPP) method. An outdoor test was conducted over four consecutive days (10–13 June 2024), comparing a conventional PV module with one fitted with the hybrid cooling system (active and passive). The cooled module achieved noticeable surface temperature reductions of 2.56 °C, 2.15 °C, 2.08 °C, and 2.58 °C across the four days. The system also delivered a peak power gain of 66.85 W, corresponding to a 2.82% efficiency improvement. Economic analysis showed that the system’s payback period is 4.52 years, with the total energy value increasing by USD 477.88, representing about a 2.81% improvement compared to the reference panel. In summary, the hybrid cooling method demonstrates clear advantages in lowering panel temperature, enhancing electrical output, and ensuring favorable economic performance. Full article

Photovoltaic (PV) energy conversion is expected to contribute to the creation of a clean energy society. For realizing such a vision, various developments such as high-efficiency, low-cost and highly reliable materials, solar cells, modules and systems are necessary. Cooperation with storage batteries is also very important for regulation and self-consumption. The creation of new applications such as building integrated PV, vehicle integrated PV, agriculture PV and floating PV is also very important for further installation of PV and reducing CO₂ emission. The sustainability of material consumption, along with reducing, reusing and recycling are also key issues for widespread deployment of PV. This paper provides an overview of the current status of photovoltaics and discusses future directions for photovoltaics from the view-points of high-efficiency, low-cost, reliability, and importance of integrated photovoltaics and sustainability. Full article

Accurate prediction of maximum voltage is essential for the safe, efficient, and sustainable design of photovoltaic systems, as it defines the maximum allowable number of modules in series. This study examines how the choice of meteorological year affects voltage estimations in high-power PV systems. A comparison is made between maximum voltage results derived from typical meteorological (TMY) years and those based on inter-hourly historical data. The results reveal notable differences, with TMY often underestimating extreme voltage levels. To address this, the study introduces the Extreme Meteorological Year (EMY) model, which uses historical voltage percentiles to better estimate peak voltages and mitigate overvoltage risk. This model has been applied successfully in real PV plant designs. Its performance is assessed using monitoring data from seven PV projects in different regions. The EMY model demonstrates improved accuracy and safety in predicting maximum voltages compared to traditional datasets. Its percentile-based structure enables adaptation to different design criteria, enhancing reliability and supporting more sustainable photovoltaic deployment. Overall, the study underscores the importance of selecting appropriate meteorological data for voltage prediction and presents EMY as a robust tool for improving PV system design. Full article

This study developed a novel folding light shelf system that integrates reflectors, photovoltaic (PV) modules, and adaptive louvers that adjust based on solar altitude, aiming to improve daylight distribution, minimize glare, and reduce energy consumption in office buildings. The research employed an advanced optimization approach, utilizing Non-dominated Sorting Genetic Algorithm III (NSGA-III) and Latin Hypercube Sampling, a highly effective method suitable for managing complex multi-objective scenarios involving numerous variables, to efficiently identify high-performance configurations with increased precision. Key design variables across all three components of the system included angle, width, distance, and the number of folds in the light shelf, along with the number of louvers. The proposed method successfully integrates PV technology into light shelves without compromising their functionality, enabling both daylight control and energy generation. The optimization results demonstrate that the system achieved up to a 15% improvement in useful daylight illuminance (UDI) and a 16% reduction in cooling energy consumption. Furthermore, the PV modules generated 509.5 kWh/year, ensuring improved efficiency and sustainability in building performance. Full article

The accurate short-term forecasting (PV) of power is crucial for grid stability control, energy trading optimization, and renewable energy integration in smart grids. However, PV generation is extremely variable and non-linear due to environmental fluctuations, which challenge the conventional forecasting models. This study proposes a hybrid deep learning architecture, Wavelet Transform–Transformer–Temporal Convolutional Network–Efficient Channel Attention Network–Gated Recurrent Unit (WT–Transformer–TCN–ECANet–GRU), to capture the overall temporal complexity of PV data through integrating signal decomposition, global attention, local convolutional features, and temporal memory. The model begins by employing the Wavelet Transform (WT) to decompose the raw PV time series into multi-frequency components, thereby enhancing feature extraction and denoising. Long-term temporal dependencies are captured in a Transformer encoder, and a Temporal Convolutional Network (TCN) detects local features. Features are then adaptively recalibrated by an Efficient Channel Attention (ECANet) module and passed to a Gated Recurrent Unit (GRU) for sequence modeling. Multiscale learning, attention-driven robust filtering, and efficient encoding of temporality are enabled with the modular pipeline. We validate the model on a real-world, high-resolution dataset of a Moroccan university building comprising 95,885 five-min PV generation records. The model yielded the lowest error metrics among benchmark architectures with an MAE of 209.36, RMSE of 616.53, and an R² of 0.96884, outperforming LSTM, GRU, CNN-LSTM, and other hybrid deep learning models. These results suggest improved predictive accuracy and potential applicability for real-time grid operation integration, supporting applications such as energy dispatching, reserve management, and short-term load balancing. Full article

In order to address the challenge of achieving optimal prediction accuracy when a single prediction model faced with changes in meteorological conditions of different weather types, this paper proposes a photovoltaic (PV) power prediction method based on the combination of similar day clustering and convolutional neural network (CNN)-gated recurrent unit (GRU). The Pearson correlation coefficient and Spearman’s correlation coefficient are used to filter out the key features such as total solar radiation and module temperature to construct a new input dataset; the K-means algorithm is used to perform clustering analysis on the data, and the data are classified into sunny, cloudy, and rainy days; the spatial correlation features of the meteorological factors are extracted by using the convolutional neural network (CNN), and the CNN-GRU model is established by combining with the gated recurrent units (GRUs). The PV output power is predicted based on the PV power data and the corresponding meteorological data from a place in Ningxia, collected during June to August 2020, and the method proposed in the article is tested. Validation results show that, compared to other models, the model proposed in this paper reduces MAE and RMSE by 66.1% and 65.7% on average under three different weather type scenarios, and improves R² by 19.8% on average. This verifies that the model has high prediction accuracy and generalization ability, achieving better results in PV output power prediction. The CNN-GRU model demonstrates superior capability in modeling short- and long-term dependencies compared to other deep learning hybrid approaches, while also achieving higher computational efficiency and faster training convergence. Full article

This study presents a comparative analysis of the annual performances of stationary and dual-axis sun-tracked photovoltaic–thermal (PVT) systems. The experimental research was conducted at a demonstration site in Oświęcim, Poland, where both systems were evaluated in terms of electricity and heat production. The test installation consisted of thirty stationary PVT modules and five dual-axis sun-tracking systems, each equipped with six PV modules. An innovative cooling system was developed for the PVT modules, consisting of a surface-mounted heat sink installed on the rear side of each panel. The system includes embedded tubes through which a cooling fluid circulates, enabling efficient heat recovery. The results indicated that the stationary PVT system outperformed a conventional fixed PV installation, whose expected output was estimated using PVGIS data. Specifically, the stationary PVT system generated 26.1 kWh/m² more electricity annually, representing a 14.8% increase. The sun-tracked PVT modules yielded even higher gains, producing 42% more electricity than the stationary system, with particularly notable improvements during the autumn and winter seasons. After accounting for the electricity consumed by the tracking mechanisms, the sun-tracked PVT system still delivered a 34% higher net electricity output. Moreover, it enhanced the thermal energy output by 85%. The findings contribute to the ongoing development of high-performance PVT systems and provide valuable insights for their optimal deployment in various climatic conditions, supporting the broader integration of renewable energy technologies in building energy systems. Full article