Research on Electricity Load Forecasting Based on EEMD-GA-LSTM

Accurate short-term load forecasting is crucial for the economic operation of power grids. In order to improve the prediction accuracy of power system load, this paper proposes a genetic algorithm (GA) optimized long-short-term memory neural network (LSTM) prediction method. The method firstly decomposes the raw numbers into several modes by integrating the empirical modal decomposition (EEMD) method, and then the LSTM network is used to make predictions respectively, and at the same time, the GA optimization algorithm is used to optimize the predict network parameters, so as to obtain the combined prediction model of EEMD-GA-LSTM. Finally, the prediction effect of the model is verified by experimental data, and the results show that the model has a good prediction effect.