An IHPO-WNN-Based Federated Learning System for Area-Wide Power Load Forecasting Considering Data Security Protection

Bujin Shi, Xinbo Zhou (), Peilin Li, Wenyu Ma and Nan Pan
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Bujin Shi: Kunming Power Supply Bureau, Yunnan Power Grid Co., Ltd., Kunming 650011, China
Xinbo Zhou: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
Peilin Li: Kunming Power Supply Bureau, Yunnan Power Grid Co., Ltd., Kunming 650011, China
Wenyu Ma: Faculty of Civil Aviation and Aeronautics, Kunming University of Science and Technology, Kunming 650500, China
Nan Pan: Faculty of Civil Aviation and Aeronautics, Kunming University of Science and Technology, Kunming 650500, China

Energies, 2023, vol. 16, issue 19, 1-20

Abstract: With the rapid growth of power demand and the advancement of new power system intelligence, smart energy measurement system data quality and security are also facing the influence of diversified factors. To solve the series of problems such as low data prediction efficiency, poor security perception, and “data islands” of the new power system, this paper proposes a federated learning system based on the Improved Hunter–Prey Optimizer Optimized Wavelet Neural Network (IHPO-WNN) for the whole-domain power load prediction. An improved HPO algorithm based on Sine chaotic mapping, dynamic boundaries, and a parallel search mechanism is first proposed to improve the prediction and generalization ability of wavelet neural network models. Further considering the data privacy in each station area and the potential threat of cyber-attacks, a localized differential privacy-based federated learning architecture for load prediction is designed by using the above IHPO-WNN as a base model. In this paper, the actual dataset of a smart energy measurement master station is selected, and simulation experiments are carried out through MATLAB software to test and examine the performance of IHPO-WNN and the federal learning system, respectively, and the results show that the method proposed in this paper has high prediction accuracy and excellent practical performance.

Keywords: electricity load forecasting; improved hunter–prey optimizer; WNN; federated learning; differential privacy (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: 2023
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