A Federated Learning Algorithm That Combines DCScaffold and Differential Privacy for Load Prediction

Yong Xiao, Xin Jin, Tingzhe Pan (), Zhenwei Yu and Li Ding
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Yong Xiao: China Southern Power Grid CSG Electric Power Research Institute, Guangzhou 510640, China
Xin Jin: China Southern Power Grid CSG Electric Power Research Institute, Guangzhou 510640, China
Tingzhe Pan: China Southern Power Grid CSG Electric Power Research Institute, Guangzhou 510640, China
Zhenwei Yu: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Li Ding: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China

Energies, 2025, vol. 18, issue 6, 1-20

Abstract: Accurate residential load forecasting plays a crucial role in optimizing demand-side resource integration and fulfilling the needs of demand-side response initiatives. To tackle challenges, such as data heterogeneity, constrained communication resources, and data security in smart grid load prediction, this study introduces a novel differential privacy federated learning algorithm. Leveraging the federated learning framework, the approach incorporates weather and temporal factors as key variables influencing load patterns, thereby creating a privacy-preserving load forecasting solution. The model is built upon the Long Short-Term Memory (LSTM) network architecture. Experimental results demonstrate that the proposed algorithm enables federated training without the need for sharing raw load data, facilitating load scheduling and energy management operations in smart grids while safeguarding user privacy. Furthermore, it exhibits superior prediction accuracy and communication efficiency compared to existing federated learning methods.

Keywords: federated learning; load forecasting; differential privacy; data heterogeneity; communication costs (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: 2025
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