Research on the Power Prediction Model of Photovoltaic Power Generation Based on Cloud Computing
Photovoltaic power generation,as a clean and renewable energy power generation mode,has been widely applied in recent years,but its randomness and volatility make power prediction extremely important.The article focuses on the study of cloud computing based photovoltaic power generation prediction models and explores the role of cloud computing in improving prediction accuracy and efficiency.We compared the performance differences of Support Vector Machines(SVM),Convolutional Neural Networks(CNN),and ensemble models(SVM+CNN)between cloud computing and local computing environments through experimental design.The experimental results show that the integrated model in cloud computing environment outperforms the local computing environment in terms of prediction accuracy,mean square error,and computational efficiency.The powerful data processing capabilities of cloud computing significantly shorten the training time of models and improve their predictive accuracy.
photovoltaic power generationpower predictioncloud computingmachine learningdata processing
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NSTL
万方数据
