Short-Term Electricity Load Forecasting Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Improved Sparrow Search Algorithm–Convolutional Neural Network–Bidirectional Long Short-Term Memory Model

Han Qiu, Rong Hu (), Jiaqing Chen () and Zihao Yuan
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Han Qiu: College of Mathematics and Statistics, Wuhan University of Technology, Wuhan 430070, China
Rong Hu: College of Mathematics and Statistics, Wuhan University of Technology, Wuhan 430070, China
Jiaqing Chen: College of Mathematics and Statistics, Wuhan University of Technology, Wuhan 430070, China
Zihao Yuan: College of Physics and Mechanics, Wuhan University of Technology, Wuhan 430070, China

Mathematics, 2025, vol. 13, issue 5, 1-32

Abstract: Accurate power load forecasting plays an important role in smart grid analysis. To improve the accuracy of forecasting through the three-level “decomposition–optimization–prediction” innovation, this study proposes a prediction model that integrates complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the improved sparrow search algorithm (ISSA), a convolutional neural network (CNN), and bidirectional long short-term memory (BiLSTM). A series of simpler intrinsic mode functions (IMFs) with different frequency characteristics can be decomposed by CEEMDAN from data, then each IMF is reconstructed based on calculating the sample entropy of each IMF. The ISSA introduces three significant enhancements over the standard sparrow search algorithm (SSA), including that the initial distribution of the population is determined by the optimal point set, the position of the discoverer is updated by the golden sine strategy, and the random walk of the population is enhanced by the Lévy flight strategy. By the optimization of the ISSA to the parameters of the CNN-BiLSTM model, integrating the prediction results of the reconstructed IMFs in the sub-models can obtain the final prediction result of the data. Through the performance indexes of the designed prediction model, the application case results show that the proposed combined prediction model has a smaller prediction error and higher prediction accuracy than the eight comparison models.

Keywords: short-term load forecasting; CEEMDAN; convolutional neural network; bidirectional long short-term memory; improved sparrow search algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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