Federated Hybrid Graph Attention Network with Two-Step Optimization for Electricity Consumption Forecasting

Hao Yang, Xinwu Ji, Qingchan Liu, Lukun Zeng (), Yuan Ai and Hang Dai
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Hao Yang: Yunnan Power Grid Co., Ltd., China Southern Power Grid, Kunming 650217, China
Xinwu Ji: Yunnan Power Grid Co., Ltd., China Southern Power Grid, Kunming 650217, China
Qingchan Liu: Yunnan Power Grid Co., Ltd., China Southern Power Grid, Kunming 650217, China
Lukun Zeng: China Southern Power Grid Digital Grid Group Co., Ltd., Guangzhou 510700, China
Yuan Ai: China Southern Power Grid Digital Grid Group Co., Ltd., Guangzhou 510700, China
Hang Dai: China Southern Power Grid Digital Grid Group Co., Ltd., Guangzhou 510700, China

Energies, 2025, vol. 18, issue 17, 1-16

Abstract: Electricity demand forecasting is essential for smart grid management, yet it presents challenges due to the dynamic nature of consumption trends and regional variability in usage patterns. While federated learning (FL) offers a privacy-preserving solution for handling sensitive, region-specific data, traditional FL approaches struggle when local datasets are limited, often leading models to overfit noisy peak fluctuations. Additionally, many regions exhibit stable, periodic consumption behaviors, further complicating the need for a global model that can effectively capture diverse patterns without overfitting. To address these issues, we propose Federated Hybrid Graph Attention Network with Two-step Optimization for Electricity Consumption Forecasting (FedHMGAT), a hybrid modeling framework designed to balance periodic trends and numerical variations. Specifically, FedHMGAT leverages a numerical structure graph with a Gaussian encoder to model peak fluctuations as dynamic covariance features, mitigating noise-driven overfitting, while a multi-scale attention mechanism captures periodic consumption patterns through hybrid feature representation. These feature components are then fused to produce robust predictions. To enhance global model aggregation, FedHMGAT employs a two-step parameter aggregation strategy: first, a regularization term ensures parameter similarity across local models during training, and second, adaptive dynamic fusion at the server tailors aggregation weights to regional data characteristics, preventing feature dilution. Experimental results verify that FedHMGAT outperforms conventional FL methods, offering a scalable and privacy-aware solution for electricity demand forecasting.

Keywords: federated learning; electricity consumption demand forecasting; graph neural network (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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