Distributed load forecasting using smart meter data: Federated learning with Recurrent Neural Networks

Load forecasting is essential for energy management, infrastructure planning, grid operation, and budgeting. Large scale smart meter deployments have resulted in ability to collect massive energy data and have created opportunities in sensor-based forecasting. Machine learning (ML) has demonstrated great successes in sensor -based load forecasting; however, when prediction is needed on a smart meter level, typically a single model is trained for each smart meter. With a large number of meters, this becomes computationally expensive or even infeasible. On the other hand, with conventional ML, training a single model for several smart meters requires participants to share their data with the central server. Consequently, this paper proposes federated learning for load forecasting with smart meter data: this strategy enables training a single model with all participating smart meters without the need to share local data. Two alternative federated learning strategies are examined: FedSGD, which performs one step of gradient descent on client before merging updates on the server, and FedAVG, which carries out several steps before the merging. Specifically, residential consumers are diverse what makes training a single model challenging as load profiles vary across consumers. The results show that the FedAVG achieves better accuracy than FedSGD while also requiring fewer communication rounds. Comparing to individual models for each meter and a single central models for all meters, FedAVG achieves comparable or better accuracy.

Automated summary

Load forecasting is crucial for energy management, infrastructure planning, and grid operation. This paper proposes the use of federated learning with smart meter data to train a single model without sharing local data. Two alternative federated learning strategies, FedSGD and FedAVG, are examined, with FedAVG achieving better accuracy and requiring fewer communication rounds.