Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning

• URL: http://arxiv.org/abs/2502.17226v1
• Date: Mon, 24 Feb 2025 15:04:29 GMT
• Title: Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning
• Authors: Ratun Rahman, Pablo Moriano, Samee U. Khan, Dinh C. Nguyen,
• Abstract summary: This paper presents a novel personalized federated learning (PFL) method for high-quality load forecasting in metering networks.<n>To minimize the load forecasting delays in our PFL model, we study a new latency optimization problem based on optimal resource allocation at SMs.<n>Our method outperforms existing approaches in terms of better load forecasting and reduced operational latency costs.
• Score: 1.1008520905907015
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Electric load forecasting is essential for power management and stability in smart grids. This is mainly achieved via advanced metering infrastructure, where smart meters (SMs) record household energy data. Traditional machine learning (ML) methods are often employed for load forecasting but require data sharing which raises data privacy concerns. Federated learning (FL) can address this issue by running distributed ML models at local SMs without data exchange. However, current FL-based approaches struggle to achieve efficient load forecasting due to imbalanced data distribution across heterogeneous SMs. This paper presents a novel personalized federated learning (PFL) method for high-quality load forecasting in metering networks. A meta-learning-based strategy is developed to address data heterogeneity at local SMs in the collaborative training of local load forecasting models. Moreover, to minimize the load forecasting delays in our PFL model, we study a new latency optimization problem based on optimal resource allocation at SMs. A theoretical convergence analysis is also conducted to provide insights into FL design for federated load forecasting. Extensive simulations from real-world datasets show that our method outperforms existing approaches in terms of better load forecasting and reduced operational latency costs.

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