Design of Short-Term Load Forecasting Model of Power System Based on LSTM

In view of the limitations of the traditional Logistic regression algorithm in design of short-term load forecasting model of electric power system, a research scheme of short-term load forecasting model design based on LSTM was proposed. Firstly, the influencing factors is accurately located through gating mechanism theories, and the indicators is reasonably divided to reduce interference, and the LSTM is used to construct a research scheme for short-term load forecasting model design. Experimental results show that under certain evaluation criteria, the proposed scheme is superior to the traditional Logistic regression algorithm in terms of research accuracy and processing time of influencing factors of short-term load forecasting model design, and has obvious advantages. The research of short-term load forecasting model design plays an extremely important role in electric power system, which can accurately predict and optimize the growth characteristics and product generation of electric power system. However, the traditional Logistic regression algorithm has some limitations in solving the problem of prediction model simulation, especially when dealing with complex problems. In this paper, this paper proposes a research scheme of short-term load forecasting model design based on LSTM to better solve this problem. The scheme accurately locates the influencing factors through gating mechanism theories, so as to determine the division of indicators, and uses the LSTM to construct the scheme. Experimental results show that under certain evaluation criteria, the accuracy and speed of the scheme is significantly improved for different problems, and it has better performance. Therefore, the simulation scheme based on LSTM in design of short-term load forecasting model of electric power system can better solve the limitations of traditional Logistic regression algorithm and improve the simulation accuracy and efficiency.