Open Access

ARTICLE

Application of Federated Learning Algorithm Based on K-Means in Electric Power Data

by Weimin He, Lei Zhao^*

State Grid Jiangsu Electric Power Co., Ltd., Nanjing, 210022, China

* Corresponding Author: Lei Zhao. Email:

Journal of New Media 2022, 4(4), 191-203. https://doi.org/10.32604/jnm.2022.032994

Received 03 June 2022; Accepted 03 July 2022; Issue published 12 December 2022

Abstract

Accurate electricity forecasting is the key basis for guiding the power sector to arrange operation plans and guaranteeing the profitability of electric power companies. However, with the increasing demand of enterprises and departments for data security, the phenomenon of “Isolated Data Island” becomes more and more serious, resulting in the accuracy loss of the traditional electricity prediction model. Federated learning, as an emerging artificial intelligence technology, is designed to ensure data privacy while carrying out efficient machine learning, which provides a new way to solve the problem of “Isolated Data Island” in terms of electricity forecasting. Nonetheless, due to the popularity of smart meters, the collected electricity data presents the characteristics of uneven distribution and huge data volume, so it is difficult to apply the electric quantity prediction model generated only by federated learning in practice. To solve this problem, a clustering federated learning method (C-FL) is proposed to protect data privacy while improving the accuracy of power prediction. Firstly, C-FL uses K-means algorithm to cluster power data locally in power enterprises, and then builds accurate power forecasting models for each class of power data combined with other local clients through federated learning. A large number of experimental results show that the clustering federated learning method proposed in this paper is superior to the existing federated learning models in terms of the accuracy of electric power forecasting.

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Keywords

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