A Collaborative Approach for Large-scale Electricity Consumption Using Federated Learning

Abstract

Forecasting energy demand is a crucial topic in the energy industry to keep the balance between supply and demand, hence keeping the grid in effective operation. The adoption of renewable energy sources for the supply makes the forecasting problem ever the more prominent because of the additional uncertainty they bring to the grid, besides the consumers’ energy usage patterns. The uncertainty on the demand side forecasting can be theoretically overcome via a centralized predictive model that takes note of the consumers’ past electricity usage. However, in practice, forecasting energy demand is challenged by users’ concerns for the privacy of their energy data and the scalability of storing it, in addition to completing the model updates in time. Both problems can be solved if the centralized training paradigm is replaced with federated training, where each household trains its model locally, and the centralized server only acts as a coordinator by aggregating the weights of the individual models’ and sending the updates back to them, all without seeing the consumers’ data. Because of the diversity in energy usage, the convergence of local models may require too much time. This study will investigate federated learning to develop a clustering algorithm that groups similar residences as one node to fasten the model convergence without reducing its accuracy.