PV Power Generation Prediction Based on DPVS Virtual Acquisition
摘要
As a kind of clean energy, photovoltaic power generation not only does not produce greenhouse gas emissions, but also helps to reduce the impact of global warming and climate change. However, due to the influence of weather conditions on photovoltaic power generation, its output fluctuates greatly, and its installation is scattered. These problems seriously limit the accuracy of photovoltaic power generation prediction. This paper proposes a reverse learning whale optimization algorithm for distributed photovoltaic virtual acquisition to improve the power prediction accuracy of distributed photovoltaic users. Firstly, a prediction model based on deep learning is built. Secondly, a reverse learning whale optimization algorithm is proposed for distributed photovoltaic virtual acquisition. Finally, the proposed method is simulated and verified.