Seasonal Short-Term Load Forecasting for Power Systems Based on Modal Decomposition and Feature-Fusion Multi-Algorithm Hybrid Neural Network Model

Abstract

To enhance the refinement of load decomposition in power systems and fully leverage seasonal change information to further improve prediction performance, this paper proposes a seasonal short-term load combination prediction model based on modal decomposition and a feature-fusion multi-algorithm hybrid neural network model. Specifically, the characteristics of load components are analyzed for different seasons, and the corresponding models are established. First, the improved complete ensemble empirical modal decomposition with adaptive noise (ICEEMDAN) method is employed to decompose the system load for all four seasons, and the new sequence is obtained through reconstruction based on the refined composite multiscale fuzzy entropy of each decomposition component. Second, the correlation between different decomposition components and different features is measured through the max-relevance and min-redundancy method to filter out the subset of features with strong correlation and low redundancy. Finally, different components of the load in different seasons are predicted separately using a bidirectional long-short-term memory network model based on a Bayesian optimization algorithm, with a prediction resolution of 15 min, and the predicted values are accumulated to obtain the final results. According to the experimental findings, the proposed method can successfully balance prediction accuracy and prediction time while offering a higher level of prediction accuracy than the current prediction methods. The results demonstrate that the proposed method can effectively address the load power variation induced by seasonal differences in different regions.