Short-term electricity load forecasting based on CEEMDAN-FE-BiGRU-Attention model

Abstract

Abstract Intending to address the volatility and intricacy of power load data, a novel method for short-term power load forecasting is presented, utilizing the CEEMDAN-FE-BiGRU-Attention model. This paper introduces the innovative adaptive noise fully integrated Empirical Mode decomposition (CEEMDAN) algorithm, which effectively decomposes the sequence into modal components of various frequencies and residual components. This decomposition serves to reduce the complexity inherent in the original time series. Subsequently, the fuzzy entropy (FE) algorithm is employed to calculate the time complexity of each component, allowing for the reconstruction of different scale sequences, thereby enhancing computational efficiency. Finally, the sequences of varying scales are input into the bidirectional recurrent neural network (BiGRU) model, incorporating the attention mechanism for prediction. Notably, the prediction accuracy of the BiGRU model with attention mechanism surpasses that of the baseline GRU model for multifeature time series, resulting in a significant improvement in prediction accuracy. Experimental findings demonstrate that the proposed model outperforms traditional approaches, enabling better capture of the variation trends in power load data while reducing time series complexity. Moreover, the proposed model exhibits a remarkable reduction in mean absolute percentage error and root mean square error values by 90.24% and 85.05%, respectively, when compared to the single BiGRU-Attention model. This enhancement enhances the accuracy of power load prediction. These innovative methodologies endow the power load forecasting method proposed in this paper with greater potential and viability for real-world applications.