Power Forecasting Model for Photovoltaic Generation System Based on Long Short Term Memory Network

In this work, power forecasting model for photovoltaic generation system based on Long Short Term Memory Network (LSTM) is presented. In the field of photovoltaic power generation, due to the strong randomness and intermittedness of photovoltaic power generation, large-scale grid-connection has a great impact on the load of energy storage grid, so photovoltaic prediction has a great reference significance for resource management and scheduling. This paper studies the photovoltaic power generation prediction method based on LSTM. With light, humidity and temperature as model input and power generation as model output, a LSTM prediction model for photovoltaic power generation prediction is constructed to solve the problem of gradient disappearance and gradient explosion. The experimental results of actual data show that the test set RMSE of the proposed model is 0.16859, R2 is 0.93203, and the prediction effect is good.