Extensive power generation prediction research based on LSTM

Electric power generation is an important part of modern society, in order to meet the growing demand for electricity and improve the reliability of the grid, the accurate prediction of electric power generation power is very important. Due to the different dimensions of the influencing factors of photovoltaic power, direct input to the prediction model may lead to poor prediction effect. Therefore, it is necessary to normalize the sample data to eliminate the influence of different dimensions on the prediction of photovoltaic power. Artificial neural network has the characteristics of elastic topology, high redundancy and nonlinear operation, and can quickly find optimal solutions, so it plays an important role. This study aims to explore a wide range of generation power prediction methods based on short term memory networks (LSTM). By analyzing historical generation power data, we build an LSTM neural network model for predicting future generation power. The model takes into account the dynamics of the time series, as well as various factors related to power generation, such as weather, load and seasonal changes. Finally, we verify the LSTM model through experiments, and the results show that the LSTM model can achieve accurate power prediction in different time ranges. The results of this study have practical implications for the power industry and power system operators to help them better plan and manage electricity production and distribution.