Research on dynamic prediction and optimization of high altitude photovoltaic power generation efficiency using GVSAO-CNN Model under 8-climate modes - Xiaoming Xiong; Heng Hu; Qiangfu Jia; Rongjian Zhang; Chongan Huang; Qingyuan Lu (Discover Applied Sciences)
Abstract In the face of mounting global energy demands and increasing environmental pressures, the transition to clean energy sources, such as photovoltaic (PV) power generation, is imperative. Nevertheless, the performance of photovoltaic (PV) systems is notably affected by intricate geographical and climatic factors, especially in areas of high altitude. Traditional forecasting techniques, which include physical models and empirical estimates, exhibit restricted effectiveness in achieving precision in predicting PV efficiency under these challenging conditions. Recent advancements in deep learning have led to the development of data-driven models; however, existing approaches frequently encounter limitations when processing high-dimensional meteorological data, which can result in the attainment of local optima. The present study proposes a novel dynamic prediction model for high-altitude PV efficiency, namely the GVSAO-CNN, which combines the Gravity Search Optimization Algorithm (GVSAO). This algorithm, as detailed in a breakthrough patent for high-altitude PV data optimization, has been shown to enhance the accuracy of solar irradiance predictions by dynamically correcting model predictions with actual observational data and Convolutional Neural Network (CNN). The GVSAO algorithm is a sophisticated optimization technique that fine-tunes the hyperparameters of CNNs. This refinement enhances the model's capacity to process high-dimensional data and circumvent local optima, thereby improving its overall performance. Recent studies have shown that the GVSAO-CNN model, which integrates an expanded one-hot encoding technique, surpasses traditional methods such as SVM, RF, and standard CNNs in enhancing prediction accuracy, computational efficiency, and stability. Specifically, it has been demonstrated to achieve a 22.6% reduction in root-mean-square error (RMSE) and a 16.7% decrease in training time when compared to traditional models. The GVSAO-CNN model, with its demonstrated ability to effectively capture dynamic efficiency changes in complex high-altitude climates, shows promise for enhancing smart grid integration, policy-making, and disaster preparedness in high-altitude regions, as evidenced by similar applications of CNN-based models in smart grid technologies. Graphical abstract