Short-Term Forecasting Method of Wind Energy and Photovoltaic Power Generation Based on Big Data Analysis

Abstract

With the continuous depletion of fossil energy and the continuous destruction of the ecological environment, wind energy(WE) and photovoltaic PG(PPG) are more and more emerging power generation(PG) technologies, and WE and PPG systems will become the mainstream of PG in my country's power grid in the future industry. PPG will be affected by the weather, resulting in great uncertainty in the PG output of the photovoltaic system(PS). If it is integrated into the large power grid, it will also bring immeasurable impact to the power grid. Therefore, in order to ensure the safe and stable operation of the power system and the coordinated development of power supply and distribution, it is crucial to use big data technology to establish a PG prediction model(PM). For wind PG, this paper establishes a short-term(ST) PG PM based on BP-NN, and proposes several correction models; for PPG, this paper establishes LS-SVM combined with Markov's PM, which can predict both sunny and rainy days. Predict the PPG in different weather. Through the research on wind PG forecasting methods and PPG forecasting methods, the accurate forecast of ST PG can be achieved, which provides certain theoretical and technical support for the PG industry of the power grid to better supply power to users in the future.