Short-Term Load Probabilistic Forecasting Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Reconstruction and Salp Swarm Algorithm

Tianyu Hu, Mengran Zhou, Kai Bian, Wenhao Lai and Ziwei Zhu
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Tianyu Hu: School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China
Mengran Zhou: School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China
Kai Bian: School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China
Wenhao Lai: School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China
Ziwei Zhu: School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China

Energies, 2021, vol. 15, issue 1, 1-14

Abstract: Short-term load forecasting is an important part of load forecasting, which is of great significance to the optimal power flow and power supply guarantee of the power system. In this paper, we proposed the load series reconstruction method combined improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) with sample entropy ( S E ). The load series is decomposed by ICEEMDAN and is reconstructed into a trend component, periodic component, and random component by comparing with the sample entropy of the original series. Extreme learning machine optimized by salp swarm algorithm (SSA-ELM) is used to predict respectively, and the final prediction value is obtained by superposition of the prediction results of the three components. Then, the prediction error of the training set is divided into four load intervals according to the predicted value, and the kernel probability density is estimated to obtain the error distribution of the training set. Combining the predicted value of the prediction set with the error distribution of the corresponding load interval, the prediction load interval can be obtained. The prediction method is verified by taking the hourly load data of a region in Denmark in 2019 as an example. The final experimental results show that the proposed method has a high prediction accuracy for short-term load forecasting.

Keywords: load forecasting; load series; mode decomposition; extreme learning machine; kernel density estimation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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