Application of Mathematical Model using Artificial Neural Network in Photovoltaic Electrical Power Generation Building

Abstract

By counting the stock data of PV building integration-related companies in Shanghai and Shenzhen stock markets, a sector index model of PV building integration was established, moving averages were calculated and moving averages were plotted, and the prediction results of the model were used to compare with known data to optimize the model, and data from April 1, 2019 to May 6, 2021 were used as the training set and data from May 6, 2021 to May 28, 2021 were used as the test set by recurrent neural network and LSTM long short-term memory artificial neural network to optimize the model. as the training set and the data from May 6, 2021 to May 28, 2021 as the test set to train the optimized model to obtain the prediction results for the next month. Using the data from April 1, 2019 to May 28, 2021, a time period of 2 months is used to analyze the correlation between the SSE index and the PV building integration sector index for each period using Pearson correlation analysis. Finally, the time series forecasting model analysis is combined to assess the investment risk of individual stocks in the PV building integration sector.