Short-Term Electricity Load Forecasting Based on NeuralProphet and CNN-LSTM

Abstract

For distribution networks, accurate short-term load forecasting is a prerequisite for the safe and stable operation as well as economically optimized dispatching of the grid. In order to enhance the accuracy of short-term power load forecasting, this paper proposes a forecasting method that combines convolutional neural network (CNN), long short-term memory (LSTM) network, and the Neuralprophet model. This method utilizes the Neuralprophet model to capture trends, seasonal cycles, holiday activities, and other components within load data, while leveraging the data feature extraction capability of the CNN model and the long-term sequence prediction ability of the LSTM model. The optimal hyperparameters of the models are determined using the Bayesian optimization algorithm, and the predictions of the two models are fused through the least squares method. Application of this method to forecasting on various load datasets demonstrates its superior prediction accuracy compared to other classical models.

Keywords

• Convolutional neural network
• hybrid model
• least squares method
• long short-term memory network
• neuralprophet
• short-term load forecasting