An Integrated Power Market Day-ahead Electricity Price Prediction Base on Combined Input Features

Fast and accurate electricity price prediction can effectively reflect the operational status of the electricity market. In addition, it is the basis for decision-making in the electricity market. To solve the difficulties in input feature selection and model construction in data-driven electricity price prediction, a method for predicting electricity market prices based on combined input features is proposed. Firstly, historical information as the original information database is collected; Secondly, select the maximum information coefficient (MIC) for feature extraction, the ReliefF feature extraction method, and the stacked denoising autoencoder (SDAE) for feature extraction as inputs; Finally, by integrating the probability outputs of multiple LSTM models, the output result of the combined feature prediction is obtained. The analysis of numerical examples shows that combining input features can effectively improve the accuracy of electricity price prediction, and the proposed combined prediction model has high accuracy.