Short-Term Energy Consumption Forecasting at the Edge: A Federated Learning Approach

Residential short-term energy consumption forecasting plays an essential role in modern decentralized power systems. The rise of innovative prediction methods able to handle the high volatility of users' electrical load has posed the basis to accomplish this task. However these methods, which mostly rely on Artificial Neural Networks, require that a huge amount of users' fine-grained sensitive consumption data are centrally collected to train a generalized forecasting model, with implications on privacy and scalability. This paper proposes an innovative architecture specifically designed to overcome this need. By exploiting Federated Learning and Edge Computing capabilities, many Long Short-Term Memory (LSTM) models are locally trained by different users based on their own historical energy consumption samples. Such models are then aggregated by a specific-purpose node to build a generalized model that is re-distributed for improved forecasting at the edge. For better forecasting, our proposed local training procedure takes as input relevant features related to calendar (i.e., hour, weekday and average consumption of previous days) and weather conditions (i.e., clustered apparent temperature), and the architecture can group users according to consumption similarities (using K-means) or socioeconomic affinities. We thoroughly evaluate the approach through simulations, showing that it can lead to similar forecasting performance than a state-of-the-art centralized solution in terms of Root Mean Square Error (RMSE), but with up to an order of magnitude lower training time and up to 50 times less exchanged data when samples are recorded at finer granularity than one hour. Nonetheless, it keeps sensitive data local and therefore guarantees users' privacy.