Search Results (1,038)

Solar irradiance prediction is a crucial component in the application of photovoltaic power generation, playing a vital role in optimizing energy production, managing energy storage, and maintaining grid stability. This paper proposes an irradiance prediction method based on a functionally structured inverted transformer network, which maintains the channel independence of each feature in the model input and extracts the correlations between different features through an Attention mechanism, enabling the model to effectively capture the relevant information between various features. After the channel mixing of different features is completed through the Attention mechanism, a linear network is used to predict the irradiance sequence. A data processing method tailored to the prediction model used in this paper is designed, which employs a comprehensive data preprocessing approach combining mutual information, multiple imputation, and median filtering to optimize the raw dataset, enhancing the overall stability and accuracy of the prediction project. Additionally, a Dingo optimization algorithm suitable for the self-tuning of deep learning model hyperparameters is designed, improving the model’s generalization capability and reducing deployment costs. The artificial intelligence (AI) model proposed in this paper demonstrates superior prediction performance compared to existing common prediction models in irradiance data forecasting and can facilitate further applications of photovoltaic power generation in power systems. Full article

As global carbon reduction initiatives progress and the new energy sector rapidly develops, photovoltaic (PV) power generation is playing an increasingly significant role in renewable energy. Accurate PV output forecasting, influenced by meteorological factors, is essential for efficient energy management. This paper presents an optimal hybrid forecasting strategy, integrating bidirectional temporal convolutional networks (BiTCN), dynamic convolution (DC), bidirectional long short-term memory networks (BiLSTM), and a novel mixed-state space model (Mixed-SSM). The mixed-SSM combines the state space model (SSM), multilayer perceptron (MLP), and multi-head self-attention mechanism (MHSA) to capture complementary temporal, nonlinear, and long-term features. Pearson and Spearman correlation analyses are used to select features strongly correlated with PV output, improving the prediction correlation coefficient (R²) by at least 0.87%. The K-Means++ algorithm further enhances input data features, achieving a maximum R² of 86.9% and a positive R² gain of 6.62%. Compared with BiTCN variants such as BiTCN-BiGRU, BiTCN-transformer, and BiTCN-LSTM, the proposed method delivers a mean absolute error (MAE) of 1.1%, root mean squared error (RMSE) of 1.2%, and an R² of 89.1%. These results demonstrate the model’s effectiveness in forecasting PV power and supporting low-carbon, safe grid operation. Full article

Most structures supporting solar panels are found on thin-walled metal piles partially driven into the ground, optimizing costs and construction time. These pile foundations are subjected to repetitive lateral loads from various external forces, such as wind, which can compromise the integrity of the pile-soil system. Given that the expected operational lifespan of photovoltaic solar plants is generally 20–30 years, predicting their service life under fatigue loads is crucial. This research analyzes the response of H-section piles to lateral fatigue loads in cohesive rigid soils through four field tests, subjected to load cycles of 55%, 72%, and 77% of the static failure load, corresponding to maximum loads of 25 kN, 32 kN, and 35 kN, respectively. Additionally, the effect of load cycles on the degradation of pile-soil adhesion is studied through two pull-out tests following cyclic tests. This study reveals that soil fatigue does not occur under repetitive loads and that soil stiffness remains constant once the cycles causing soil compaction have been overcome. Nevertheless, the accumulated plastic deflection of the soil increases steadily once soil compaction occurs due to cyclic loading. The implications of these results on the fatigue life of photovoltaic solar panel foundations are discussed. Full article

Distributed photovoltaic power stations have advantages such as local direct power supply and reduced transmission energy consumption, and whose demands are constantly being developed. Conducting research on medium- and long-term distributed photovoltaic prediction will have significant value for applications such as the electricity trade market, power grid operation, and the planning of new power stations. Due to characteristics such as long time dependence, disperse power stations, and strong randomness, making accurate and stable predictions becomes very difficult. In this research, we propose a multiple time series feature and multiple-model fusion-based ensemble learning model for medium- and long-term distributed photovoltaic power prediction (M2E-DPV). Considering the wave influence and the differences in distributions in different areas of photovoltaic power, multiple feature combinations are designed to increase feature expression ability and adaptability. Based on the boost ensemble learning model, trained on a single model of different time scale features, the optimal scoring strategy is used for multiple model fusion in the rolling prediction process, and finally, time-segmented probabilistic correction is performed. The experiment results show the effectiveness of the M2E-DPV under multiple feature combinations and multi-model fusion strategies. The average MAPE, R², and ACC indicators are 0.15, 0.96, and 0.91, respectively. Compared with other methods, there is a significant improvement, indicating that the prediction ability of the model framework proposed in this paper is advanced and robust. Full article

The life cycle of a microgrid covers all the stages from idea to implementation, through exploitation until the end of its life, with a lifespan of around 25 years. Covering them usually requires several software tools, which can make the integration of results from different stages difficult and may imply costs being hard to estimate from the beginning of a project. This paper proposes a unified platform composed of four modules developed in MATLAB 2022b, designed to assist all the processes a microgrid passes through during its lifetime. This entire platform can be used by a user with low IT knowledge, because it is completed with fill-in-the-blank alone, as a major advantage. The authors detail the architecture, functions and development of the platform, either by highlighting the novel integration of existing MATLAB tools or by developing new ones and designing new user interfaces linked with scripts based on its complex mathematical libraries. By consolidating processes into a single platform, the proposed solution enhances integration, reduces complexity and provides better cost predictability throughout the project’s duration. A proof-of-concept for this platform was presented by applying the life-cycle assessment process on a real-case study, a microgrid consisting of a photovoltaic plant, and an office building as the consumer and energy storage units. This platform has also been developed by involving students within summer internships, as a process strengthening the cooperation between industry and academia. Being an open-source application, the platform will be used within the educational process, where the students will have the possibility to add functionalities, improve the graphical representation, create new reports, etc. Full article

The advancement to Urban 4.0 requires urban digitization and predictive maintenance of infrastructure to improve efficiency, durability, and quality of life. This study aims to integrate intelligent technologies for the predictive maintenance of photovoltaic panel systems, which serve as essential smart city renewable energy sources. In addition, we employ vision transformers (ViT), a deep learning architecture devoted to evolving image analysis, to detect anomalies in PV systems. The ViT model is pre-trained on ImageNet to exploit a comprehensive set of relevant visual features from the PV images and classify the input PV panel. Furthermore, the developed system was integrated into a web application that allows users to upload PV images, automatically detect anomalies, and provide detailed panel information, such as PV panel type, defect probability, and anomaly status. A comparative study using several convolutional neural network architectures (VGG, ResNet, and AlexNet) and the ViT transformer was conducted. Therefore, the adopted ViT model performs excellently in anomaly detection, where the ViT achieves an AUC of 0.96. Finally, the proposed approach excels at the prompt identification of potential defects detection, reducing maintenance costs, advancing equipment lifetime, and optimizing PV system implementation. Full article

Predicting solar power generation is a complex challenge with multiple issues, such as data quality and choice of methods, which are crucial to effectively integrate solar power into power grids and manage photovoltaic plants. This study creates a hybrid methodology to improve the accuracy of short-term power prediction forecasts using a model called Transformer Bi-LSTM (Bidirectional Long Short-Term Memory). This model, which combines elements from the transformer architecture and bidirectional LSTM (Long–Short-Term Memory), is evaluated using two strategies: the first strategy makes a direct prediction using meteorological data, while the second employs a chain of deep learning models based on transfer learning, thus simulating the traditional physical chain model. The proposed approach improves performance and allows you to incorporate physical models to refine forecasts. The results outperform existing methods on metrics such as mean absolute error, specifically by around 24%, which could positively impact power grid operation and solar adoption. Full article

In recent years, nitrate has emerged as a significant groundwater pollutant due to its potential ecotoxicity. In particular, nitrate contamination of brackish groundwater poses a serious threat to both ecosystems and human health and remains difficult to treat. A promising, sustainable, and environmentally friendly solution when biological treatments are not applicable is the conversion of nitrate to harmless nitrogen (N₂) or ammonia (NH₃) as a nutrient by electrocatalytic nitrate reduction (eNO₃R) using solar photovoltaic energy. This review provides a comprehensive overview of the current advances in eNO₃R for the production of nitrogen and ammonia. The discussion begins with fundamental concepts, including a detailed examination of the mechanisms and pathways involved, supported by Density Functional Theory (DFT) to elucidate specific aspects of ammonium and nitrogen formation during the process. Furthermore, the integration of artificial intelligence (AI) and machine learning (ML) offers promising advancements in enhancing the predictive power of DFT, accelerating the discovery and optimization of novel catalysts. In this review, we also explore various electrode preparation methods and emphasize the importance of in situ characterization techniques to investigate surface phenomena during the reaction process. The review highlights numerous examples of copper-based catalysts and analyses their feasibility and effectiveness in ammonia production. It also explores strategies for the conversion of nitrate to N₂, focusing on nanoscale zerovalent iron as a selective material and the subsequent oxidation of the produced ammonia. Finally, this review addresses the implementation of the eNO₃R process for the treatment of brackish groundwater, discussing various challenges and providing reasonable opinions on how to overcome these obstacles. By synthesizing current research and practical examples, this review highlights the potential of eNO₃R as a viable solution to mitigate nitrate pollution and improve water quality. Full article

Qatar identified that food supply security, including self-sufficiency in vegetable production and increasing sustainable renewable energy generation, is important for increasing economic and environmental resiliency. Very favorable solar energy resources in Qatar suggest opportunities to simultaneously meet this goal by integrating solar energy generation and food production. This study examines the feasibility of developing a sustainable agri-photovoltaic (APV) greenhouse design. A comprehensive greenhouse with solar energy generation included is developed for year-round operation in Lusail, Qatar. The performance of the system is predicted by integrating meteorological data and MATLAB simulations of system components. Important design considerations included optimizing solar energy generation by fixed solar photovoltaic panels placed on the maximum available surface area of the greenhouse canopy, while balancing crop insolation and energy needs for greenhouse HVAC systems. Electrical energy is also stored in an industrial battery. Results suggest the APV greenhouse is technically and economically viable and that it could provide benefits, including enhancing food security, promoting renewable energy, and contributing to sustainable food and energy production in Qatar. Full article

Photovoltaic power generation is influenced not only by variable environmental factors, such as solar radiation, temperature, and humidity, but also by the condition of equipment, including solar modules and inverters. In order to preserve energy production, it is essential to maintain and operate the equipment in optimal condition, which makes it crucial to determine the condition of the equipment in advance. This paper proposes a method of determining a degradation of efficiency by focusing on photovoltaic equipment, especially inverters, using LSTM (Long Short-Term Memory) for maintenance. The deterioration in the efficiency of the inverter is set based on the power generation predicted through the LSTM model. To this end, a correlation analysis and a linear analysis were performed between the power generation data collected at the power plant to learn the power generation prediction model and the data collected by the environmental sensor. With this analysis, a model was trained using solar radiation data and power data that are highly correlated with power generation. The results of the evaluation of the model’s performance show that it achieves a MAPE of 7.36, an RMSE of 27.91, a MAE of 18.43, and an R2 of 0.97. The verified model is applied to the power generation data of the selected inverters for the years 2020, 2021, and 2022. Through statistical analysis, it was determined that the error rate in 2022, the third year of its operation, increased by 159.55W on average from the error rate of the power generation forecast in 2020, the first year of operation. This indicates a 0.75% decrease in the inverter’s efficiency compared to the inverter’s power generation capacity. Therefore, it is judged that it can be applied effectively to analyses of inverter efficiency in the operation of photovoltaic plants. Full article

With the large-scale development of solar power generation, highly uncertain photovoltaic (PV) power output has an increasing impact on distribution networks. PV power generation has complex correlations with various weather factors, while the time series embodies multiple temporal characteristics. To more accurately quantify the uncertainty of PV power generation, this paper proposes a short-term PV power probabilistic forecasting method based on the combination of decomposition prediction and multidimensional variable dependency modeling. First, a seasonal and trend decomposition using a Loess (STL)-based PV time series feature decomposition model is constructed to obtain periodic, trend, and residual components representing different characteristics. For different components, this paper develops a periodic component prediction model based on TimeMixer for multi-scale temporal feature mixing, a long short-term memory (LSTM)-based trend component extraction and prediction model, and a multidimensional PV residual probability density prediction model optimized by Vine Copula optimized with Q-Learning. These components’ results form a short-term PV probabilistic forecasting method that considers both temporal features and multidimensional variable correlations. Experimentation with data from the Desert Knowledge Australia Solar Center (DKASC) demonstrates that the proposed method reduced root mean square error (RMSE) and mean absolute percentage error (MAPE) by at least 14.8% and 22%, respectively, compared to recent benchmark models. In probability interval prediction, while improving accuracy by 4% at a 95% confidence interval, the interval width decreased by 19%. The results show that the proposed approach has stronger adaptability and higher accuracy, which can provide more valuable references for power grid planning and decision support. Full article

A novel prototype based on the combination of a multi-junction, high-efficiency photovoltaic (PV) module and a supercapacitor (SC) able to self-power a wireless sensor node (WSN) for outdoor air quality monitoring has been developed and tested. A PV module with about an 8 cm² active area made of eight GaAs-based triple-junction solar cells with a nominal 29% efficiency was assembled and characterized under terrestrial clear-sky conditions. Energy is stored in a 4000 F/4.2 V supercapacitor with high energy capacity and a virtually infinite lifetime (10⁴ cycles). The node power consumption was tailored to the typical power consumption of miniaturized, low-consumption NDIR CO₂ sensors relying on an LED as the IR source. The charge/discharge cycles of the supercapacitor connected to the triple-junction PV module were measured under illumination with a Sun Simulator device at selected radiation intensities and different node duty cycles. Tests of the miniaturized prototype in different illumination conditions outdoors were carried out. A model was developed from the test outcomes to predict the maximum number of sensor samplings and data transmissions tolerated by the node, thus optimizing the WSN operating conditions to ensure its self-powering for years of outdoor deployment. The results show the self-powering ability of the WSN node over different insolation periods throughout the year, demonstrating its operation for a virtually unlimited lifetime without the need for battery substitution. Full article

The integration of photovoltaic and electric vehicles in distribution networks is rapidly increasing due to the shortage of fossil fuels and the need for environmental protection. However, the randomness of photovoltaic and the disordered charging loads of electric vehicles cause imbalances in power flow within the distribution system. These imbalances complicate voltage management and cause economic inefficiencies in power dispatching. This study proposes an innovative economic strategy utilizing battery energy storage system and electric vehicles cooperation to achieve voltage regulation in photovoltaic-connected distribution system. Firstly, a novel pelican optimization algorithm-XGBoost is introduced to enhance the accuracy of photovoltaic power prediction. To address the challenge of disordered electric vehicles charging loads, a wide-local area scheduling method is implemented using Monte Carlo simulations. Additionally, a scheme for the allocation of battery energy storage system and a novel slack management method are proposed to optimize both the available capacity and the economic efficiency of battery energy storage system. Finally, we recommend a day-ahead real-time control strategy for battery energy storage system and electric vehicles to regulate voltage. This strategy utilizes a multi-particle swarm algorithm to optimize economic power dispatching between battery energy storage system on the distribution side and electric vehicles on the user side during the day-ahead stage. At the real-time stage, the superior control capabilities of the battery energy storage system address photovoltaic power prediction errors and electric vehicle reservation defaults. This study models an IEEE 33 system that incorporates high-penetration photovoltaics, electric vehicles, and battery storage energy systems. A comparative analysis of four scenarios revealed significant financial benefits. This approach ensures economic cooperation between devices on both the user and distribution system sides for effective voltage management. Additionally, it encourages trading activities of these devices in the power market and establishes a foundation for economic cooperation between devices on both the user and distribution system sides. Full article

Prediction of solar irradiance is crucial for photovoltaic energy generation, as it helps mitigate intermittencies caused by atmospheric fluctuations such as clouds, wind, and temperature. Numerous studies have applied machine learning and deep learning techniques from artificial intelligence to address this challenge. Based on the recently proposed Hybrid Prediction Method (HPM), this paper presents an original and comprehensive dataset with nine attributes extracted from all-sky images developed using image processing techniques. This dataset and analysis of its attributes offer new avenues for research into solar irradiance forecasting. To ensure reproducibility, the data processing workflow and the standardized dataset have been meticulously detailed and made available to the scientific community to promote further research into prediction methods for photovoltaic energy generation. Full article

Solar energy plays a major role in the transition to renewable energy. To ensure that large-scale photovoltaic (PV) power plants operate at their full potential, their monitoring is essential. It is common practice to utilize drones equipped with infrared thermography (IRT) cameras to detect defects in modules, as the latter can lead to deviating thermal behavior. However, IRT images can also show temperature hot-spots caused by inhomogeneous soiling on the module’s surface. Hence, the method does not differentiate between defective and soiled modules, which may cause false identification and economic and resource loss when replacing soiled but intact modules. To avoid this, we propose to detect spatially inhomogeneous soiling losses and model temperature variations explained by soiling. The spatially resolved soiling information can be obtained, for example, using aerial images captured with ordinary RGB cameras during drone flights. This paper presents an electrothermal model that translates the spatially resolved soiling losses of PV modules into temperature maps. By comparing such temperature maps with IRT images, it can be determined whether the module is soiled or defective. The proposed solution consists of an electrical model and a thermal model which influence each other. The electrical model of Bishop is used which is based on the single-diode model and replicates the power output or consumption of each cell, whereas the thermal model calculates the individual cell temperatures. Both models consider the given soiling and weather conditions. The developed model is capable of calculating the module temperature for a variety of different weather conditions. Furthermore, the model is capable of predicting which soiling pattern can cause critical hot-spots. Full article