Application of CNN-LSTM based hybrid neural network in power load forecasting

Abstract Effective prediction of electrical energy consumption, rational formulation of corresponding safety measures, and improvement of the accuracy of power load time series prediction are important guidelines for improving the application and management of electrical energy. In order to accurately predict the electric energy consumption and enhance the applicability of the model. In this paper, we propose a convolutional neural network (CNN) based on electric energy consumption data combined with a long-term short-term memory recurrent neural network (LSTM) for electric energy consumption prediction model, selecting electric energy consumption time series with large samples and large time span. consumption time series, including model structure design, model training, model prediction, and model optimization, to implement the prediction algorithm. By using the minimum objective function as the optimization objective, the Adam optimization algorithm is used to continuously update the weights of the neural network and to tune the network layers and batch size to select the best. The number of layers and batch size are used as parameters of the power consumption prediction model. Finally, the optimized CNN-LSTM prediction model is invoked to predict the electricity consumption in the next time period using the electricity load data of Interconnection LLC (PJM) under the Regional Transmission Organization (RTO) in the United States as an example. The results show that the combined model can achieve 98.94% prediction accuracy and 0.0066 mean absolute error (MAE), all of which are better than other basic models, proving that the combined prediction model has better performance in terms of power load prediction accuracy.