Search Results (589)

In recent years, solar energy has emerged as a pillar of sustainable development. However, maintaining panel efficiency under extreme environmental conditions remains a persistent hurdle. This study introduces an automated defect detection pipeline that leverages deep learning and computer vision to identify five standard anomaly classes: Non-Defective, Dust, Defective, Physical Damage, and Snow on photovoltaic surfaces. To build a robust foundation, a heterogeneous dataset of 8973 images was sourced from public repositories and standardized into a uniform labeling scheme. This dataset was then expanded through an aggressive augmentation strategy, including flips, rotations, zooms, and noise injections. A YOLOv11-based model was trained and fine-tuned using both fixed and adaptive learning rate schedules, achieving a mAP@0.5 of 85% and accuracy, recall, and F1-score above 95% when evaluated across diverse lighting and dust scenarios. The optimized model is integrated into an interactive dashboard that processes live camera streams, issues real-time alerts upon defect detection, and supports proactive maintenance scheduling. Comparative evaluations highlight the superiority of this approach over manual inspections and earlier YOLO versions in both precision and inference speed, making it well suited for deployment on edge devices. Automating visual inspection not only reduces labor costs and operational downtime but also enhances the longevity of solar installations. By offering a scalable solution for continuous monitoring, this work contributes to improving the reliability and cost-effectiveness of large-scale solar energy systems. Full article

With increasing energy demand and advancing carbon neutrality goals, arid regions—key areas for centralized photovoltaic (PV) station development in China—urgently require efficient and accurate remote sensing techniques to support spatial distribution monitoring and ecological impact assessment. Although numerous studies have focused on PV station extraction, challenges remain in arid regions with complex surface features to develop extraction frameworks that balance efficiency and accuracy at a regional scale. This study focuses on the Inner Mongolia Yellow River Basin and develops a PV extraction framework on the Google Earth Engine platform by integrating spectral bands, spectral indices, and topographic features, systematically comparing the classification performance of support vector machine, classification and regression tree, and random forest (RF) classifiers. The results show that the RF classifier achieved a high Kappa coefficient (0.94) and F1 score (0.96 for PV areas) in PV extraction. Feature importance analysis revealed that the Normalized Difference Tillage Index, near-infrared band, and Land Surface Water Index made significant contributions to PV classification, accounting for 10.517%, 6.816%, and 6.625%, respectively. PV stations are mainly concentrated in the northern and southwestern parts of the study area, characterized by flat terrain and low vegetation cover, exhibiting a spatial pattern of “overall dispersion with local clustering”. Landscape pattern indices further reveal significant differences in patch size, patch density, and aggregation level of PV stations across different regions. This study employs Sentinel-2 imagery for regional-scale PV station extraction, providing scientific support for energy planning, land use optimization, and ecological management in the study area, with potential for application in other global arid regions. Full article

The climatic conditions of a region are a constant object of study, especially now that climate change is clearly affecting quality of life and the way we live. The study of the climatic conditions of a region is conducted through meteorological data. Meteorological installations include a set of sensors to monitor the meteorological and climatic conditions of an area. Meteorological data parameters include measurements of temperature, humidity, precipitation, wind speed, and direction, as well as tools such as an oratometer and a pyranometer, etc. Specifically, the pyranometer is a high-cost instrument, which has the ability to measure the intensity of the sunshine on the surface of the earth, expressing the measurement in Watt/m². Pyranometers have many applications. They can be used to monitor solar energy in a given area, in automated systems such as photovoltaic system management, or in automatic building shading systems. In this research, both the implementation and the evaluation of an integrated low-cost pyranometer system is presented. The proposed pyranometer device consists of affordable modules, both microprocessor and sensor. In addition, a central server, as the information system, was created for data collection and visualization. The data from the measuring system is transmitted via a wireless network (Wi-Fi) over the Internet to an information system (central server), which includes a database for collecting and storing the measurements, and visualization software. The end user can retrieve the information through a web page. The results are encouraging, as they show a satisfactory degree of determination of the measurements of the proposed low-cost device in relation to the reference measurements. Finally, a correction function is presented, aiming at more reliable measurements. 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 aims to evaluate the feasibility and benefits of integrating photovoltaic (APV) systems with rice cultivation, focusing on growth characteristics, chlorophyll content and fluorescence, yield components, and electricity production. An APV system was installed over a rice paddy area in Namhae-gun, Gyeongsangnam-do, with 607 modules providing a total capacity of approximately 97.12 kW. The Baegokchal variety of rice was cultivated following standard practices, and growth characteristics, chlorophyll content, and fluorescence were measured throughout the growing period. Yield components were analyzed, and electricity production was monitored to assess the performance of the APV system. The rice growing period in 2021 experienced lower than average temperatures and higher rainfall. Despite these conditions, rice in the APV systems showed increased chlorophyll content and fluorescence, indicating an adaptive response to reduced sunlight. Rice plants in APV systems exhibited greater plant height but fewer tillers compared to the control group. Leaves were significantly longer and wider, enhancing photosynthetic efficiency under shading. The yield of rice in APV systems was reduced by approximately 9% compared to the control, less severe than that reported in other studies. The APV system demonstrated stable electricity production, with consistent output throughout the year, despite variations in solar radiation. Integrating photovoltaic systems with rice cultivation is feasible and beneficial, providing a reliable source of renewable energy and enhancing farm income despite a slight reduction in rice yield. This study highlights the potential of APV systems to contribute to sustainable agriculture and renewable energy expansion, suggesting the need for further research on various crops and conditions to optimize system performance. Full article

Partial shading condition (PSC) for photovoltaic (PV) arrays complicates the operation of PV systems at peak power due to the existence of multiple peak points on the power–voltage (P–V) characteristic curve. Identifying the global peak among multiple peaks presents challenges, as the system may become trapped at a local peak, potentially resulting in significant power loss. Power generation is reduced, and hot-spot issues might arise, which can cause shaded modules to fail, under the partly shaded case. In this paper, instead of focusing on local peaks, several effective, precise, and dependable maximum power point tracker (MPPT) systems monitor the global peak using a fuzzy logic controller. The suggested method can monitor the total of all PV array peaks using an interleaved boost converter DC/DC (IBC), not only the global peaks. A DC/DC class boost converter (CBC), the current gold standard for traditional control methods, is pitted against the suggested converter. Four PSC-PV systems employ three-phase inverters to connect their converters to the power grid. Full article

Efficiently controlling and managing photovoltaic installations for self-consumption is key to making optimal use of these systems. The first step to achieving that control is modeling the generation of electricity by photovoltaic panels with digital twins. This paper presents an experimental evaluation of four photovoltaic power prediction models in a real-world environment using high-resolution, annual data from a university facility. A mathematical model and three neural network models (MLP, LSTM, and GRU) are compared under standardized metrics. The results demonstrate that the Multilayer Perceptron (MLP) achieves the highest overall accuracy and seasonal consistency. It outperforms the other three models, particularly in scenarios where the relationships between meteorological variables and power generation are predominantly static. However, the mathematical model maintains a competitive performance in conditions of low variability, such as in the summer and autumn. The seasonal analysis reveals the importance of selecting and adjusting models according to operational and climatic contexts. This study’s main contribution is identifying the MLP as the most efficient option for implementing digital twins in photovoltaic installation control. This facilitates real-time monitoring, optimization, and predictive maintenance and contributes to smart, sustainable energy management in buildings to align with climate neutrality guidelines. Full article

Modernizing irrigation systems (ISs) from traditional gravity methods to sprinkler and drip technologies has significantly improved water use efficiency. However, it has simultaneously increased electricity demand and operational costs. Integrating photovoltaic generators into ISs represents a promising solution, as solar energy availability typically aligns with peak irrigation periods. Despite this potential, photovoltaic pumping systems (PVPSs) often face reliability issues due to fluctuations in solar irradiance, resulting in frequent start/stop cycles and premature equipment wear. The IEC 62253 standard establishes procedures for evaluating PVPS performance but primarily addresses steady-state conditions, neglecting transient regimes. As the main contribution, the current paper proposes a non-intrusive, high-resolution monitoring system and a methodology to assess the performance of an isolated, high-power PVPS, considering also transient regimes. The system records critical electrical, hydraulic and environmental parameters every second, enabling in-depth analysis under various weather conditions. Two performance indicators, pumped volume efficiency and equivalent operating time, were used to evaluate the system’s performance. The results indicate that near-optimal performance is only achievable under clear sky conditions. Under the appearance of clouds, control strategies designed to protect the system reduce overall efficiency. The proposed methodology enables detailed performance diagnostics and supports the development of more robust PVPSs. Full article

This paper presents an annual, real-world evaluation of the performance and economics of a residential photovoltaic (PV) system coupled with a battery energy storage system (BESS) in southern Poland. The system, monitored with 5 min resolution, operated under time-of-use (TOU) electricity tariffs. Seasonal variation was significant; self-sufficiency exceeded 90% in summer, while winter conditions increased grid dependency. The hybrid system reduced electricity costs by over EUR 1400 annually, with battery operation optimized for high-tariff periods. Comparative analysis of three configurations—grid-only, PV-only, and PV + BESS—demonstrated the economic advantage of the integrated solution, with the shortest payback period (9.0 years) achieved with financial support. However, grid voltage instability during high PV production led to inverter shutdowns, highlighting limitations in the infrastructure. This study emphasizes the importance of tariff strategies, environmental conditions, and voltage control when designing residential PV-BESS systems. Full article

Improving the energy efficiency of residential buildings is essential for achieving global climate goals and reducing environmental impact. This study analyzes the Total Performance approach using the example of a modern semi-detached house built by a Polish developer, as an example. The building is designed with integrated systems that minimize energy consumption while maintaining resident comfort. The building is equipped with an air-to-water heat pump, underfloor heating, mechanical ventilation with heat recovery, and automatic temperature control systems. Energy efficiency was assessed using ArCADia–TERMOCAD 8.0 software in accordance with Polish Technical Specifications (TS) and verified by monitoring real-time electricity consumption during the heating season. The results show a PED from non-renewable sources of 54.05 kWh/(m²·year), representing a 23% reduction compared to the Polish regulatory limit of 70 kWh/(m²·year). Real-time monitoring conducted from December 2024 to April 2025 confirmed these results, indicating an actual energy demand of approximately 1771 kWh/year. Domestic hot water (DHW) preparation accounted for the largest share of energy consumption. Despite its dependence on grid electricity, the building has the infrastructure to enable future photovoltaic (PV) installation, offering further potential for emissions reduction. The results confirm that Total Performance strategies are not only compliant with applicable standards, but also economically and environmentally viable. They represent a scalable model for sustainable residential construction, in line with the European Union’s (EU’s) decarbonization policy and the goals of the European Green Deal. Full article

Renewable Energy Communities (RECs) are voluntary coalitions of citizens, small and medium-sized enterprises and local authorities, which cooperate to share locally produced renewable energy, providing environmental, economic, and social benefits rather than profits. Despite a favorable European and Italian regulatory framework, their development is still limited in the Member States. To this end, this paper proposes a methodology to identify optimal spatial configurations of RECs, based on proximity criteria and maximization of energy self-sufficiency. This result is achieved through the mapping of the demand, expressive of the energy consumption of residential buildings; the suitable areas for installing photovoltaic panels on the roofs of existing buildings; the supply; the supply–demand balance, from which it is possible to identify Positive Energy Districts (PEDs) and Negative Energy Districts (NEDs). Through an iterative process, the optimal configuration is then sought, aggregating only PEDs and NEDs that meet the chosen criteria. This method is applied to the case study of the Avellino Province in the Campania Region (Italy). The maps obtained allow local authorities to inform citizens about the areas where it is convenient to aggregate with their neighbors in a REC to have benefits in terms of energy self-sufficiency, savings on bills or incentives at the local level, including those deriving from urban plans. The latter can encourage private initiative in order to speed up the RECs’ deployment. The presented model is being implemented in the framework of an ongoing research and development project, titled Renewable Energy Communities Monitoring, Optimization, and Planning (RECMOP). Full article

This article presents the development, integration, and experimental validation of a modular microgrid for sustainable hydrogen production, addressing global electricity demand and environmental challenges. The system was designed for initial validation in a thermoelectric power plant environment, with scalability to other applications. Centered on a six-compartment skid, it integrates photovoltaic generation, battery storage, and a liquefied petroleum gas generator to emulate typical cogeneration conditions, together with a high-purity proton exchange membrane electrolyzer. A supervisory control module ensures real-time monitoring and energy flow management, following international safety standards. The study also explores the incorporation of blockchain technology to certify the renewable origin of hydrogen, enhancing traceability and transparency in the green hydrogen market. The experimental results confirm the system’s technical feasibility, demonstrating stable hydrogen production, efficient energy management, and islanded-mode operation with preserved grid stability. These findings highlight the strategic role of hydrogen as an energy vector in the transition to a cleaner energy matrix and support the proposed architecture as a replicable model for industrial facilities seeking to combine hydrogen production with advanced microgrid technologies. Future work will address large-scale validation and performance optimization, including advanced energy management algorithms to ensure economic viability and sustainability in diverse industrial contexts. Full article

Precise photovoltaic (PV) performance modeling is essential for optimizing system design, operational monitoring, and reliable power forecasting—yet spectral correction is often overlooked, despite its significant impact on energy yield uncertainty. This study employs the FARMS-NIT model to assess the impact of spectral irradiance on eight PV technologies across 79 European sites, grouped by Köppen–Geiger climate classification. Unlike previous studies limited to clear-sky or single-site analysis, this work integrates satellite-derived spectral data for both all-sky and clear-sky scenarios, enabling hourly, tilt-optimized simulations that reflect real-world operating conditions. Spectral analyses reveal European climates exhibit blue-shifted spectra versus AM1.5 reference, only 2–5% resembling standard conditions. Thin-film technologies demonstrate superior spectral gains under all-sky conditions, though the underlying drivers vary significantly across climatic regions—a distinction that becomes particularly evident in the clear-sky analysis. Crystalline silicon exhibits minimal spectral sensitivity (<1.6% variations), with PERC/PERT providing highest stability. CZTSSe shows latitude-dependent performance with ≤0.7% variation: small gains at high latitudes and losses at low latitudes. Atmospheric parameters were analyzed in detail, revealing that air mass (AM), clearness index (K_t), precipitable water (W), and aerosol optical depth (AOD) play key roles in shaping spectral effects, with different parameters dominating in distinct climate groups. Full article

Nearly zero-energy buildings (nZEBs) are central to global carbon reduction strategies, and Taiwan is actively promoting their adoption through building energy performance labeling, particularly in the retrofit of existing buildings. Under Taiwan’s nZEB framework, qualification requires both an A⁺ energy performance label and over 50% energy savings from retrofit technologies. This study proposes an integrated assessment framework for retrofitting small- to medium-sized office buildings into nZEBs, incorporating diagnostics, technical evaluation, policy alignment, and resource integration. A case study of a bank branch in Kaohsiung involved on-site energy monitoring and EnergyPlus V22.2 simulations to calibrate and assess the retrofit impacts. Lighting improvements and two HVAC scenarios—upgrading the existing fan coil unit (FCU) system and adopting a completely new variable refrigerant flow (VRF) system—were evaluated. The FCU and VRF scenarios reduced the energy use intensity from 141.3 to 82.9 and 72.9 kWh/m²·yr, respectively. Combined with rooftop photovoltaics and green power procurement, both scenarios met Taiwan’s nZEB criteria. The proposed framework demonstrates practical and scalable strategies for decarbonizing existing office buildings, supporting Taiwan’s 2050 net-zero target. Full article

With the large-scale global deployment of photovoltaics (PV), traditional monitoring technologies face challenges such as wiring difficulties, high energy consumption, and high maintenance costs in remote or complex terrains, which limit long-term environmental sensing. Therefore, energy-harvesting systems are crucial for the intelligent operation of photovoltaic systems; however, their deployment depends on the accurate mapping of wind energy fields and solar irradiance fields. This study proposes a multi-scale simulation method based on computational fluid dynamics (CFD) to optimize the placement of energy-harvesting systems in photovoltaic power plants. By integrating wind and irradiance distribution analysis, the spatial characteristics of airflow and solar radiation are mapped to identify high-efficiency zones for energy harvesting. The results indicate that the top of the photovoltaic panel exhibits a higher wind speed and reflected irradiance, providing the optimal location for an energy-harvesting system. The proposed layout strategy improves overall energy capture efficiency, enhances sensor deployment effectiveness, and supports intelligent, maintenance-free monitoring systems. This research not only provides theoretical guidance for the design of energy-harvesting systems in PV stations but also offers a scalable method applicable to various geographic scenarios, contributing to the advancement of smart and self-powered energy systems. Full article