A Short-Term Power Load Forecasting Method of Based on the CEEMDAN-MVO-GRU

Taorong Jia, Lixiao Yao, Guoqing Yang () and Qi He
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Taorong Jia: School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
Lixiao Yao: School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
Guoqing Yang: School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
Qi He: School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China

Sustainability, 2022, vol. 14, issue 24, 1-18

Abstract: Given that the power load data are stochastic and it is difficult to obtain accurate forecasting results by a single algorithm. In this study, a combined forecasting method for short-term power load was proposed based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Multiverse optimization algorithm (MVO), and the Gated Recurrent Unit (GRU) based on Rectified Adam (RAdam) optimizer. Firstly, the model uses the CEEMDAN algorithm to decompose the original electric load data into subsequences of different frequencies, and the dominant factors are extracted from the subsequences. Then, a GRU network based on the RAdam optimizer was built to perform the forecasting of the subsequences using the existing subsequences data and the associated influencing factors as the data set. Meanwhile, the parameters of the GRU network were optimized with the MVO optimization algorithm for the prediction problems of different subsequences. Finally, the prediction results of each subsequence were superimposed to obtain the final prediction results. The proposed combined prediction method was implemented in a case study of a substation in Weinan, China, and the prediction accuracy was compared with the traditional prediction method. The prediction accuracy index shows that the Root Mean Square Error of the prediction results of the proposed model is 80.18% lower than that of the traditional method, and the prediction accuracy error is controlled within 2%, indicating that the proposed model is better than the traditional method. This will have a favorable impact on the safe and stable operation of the power grid.

Keywords: short-term power load forecasting; CEEMDAN; RAdam; GRU; MVO (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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