Privacy-Preserving Energy Recommendations Using Federated Learning and Local LLMs on the Edge
Christos Chronis, Iraklis Varlamis, George J. Dimitrakopoulos, Fayçal Bensaali, Georgios Th. Papadopoulos · 2024
Effective energy management in households is critical to achieving overall energy efficiency and sustainability goals. This study introduces a novel approach to predicting short-term energy consumption for households using federated learning (FL) models. The approach achieves short-term energy consumption predictions (i.e. for the next 10 minutes) by analyzing local data, such as the current watt consumption and activated devices. The key innovation in this approach is the use of privacy-preserving machine learning techniques, ensuring that personal data is never shared during the training process. The models are used to predict potential spikes in energy demand, allowing for proactive management. In addition, a local Large Language Model (LLM) is integrated to generate personalized recommendations for users, aimed at avoiding predicted consumption spikes and promoting energy-efficient behavior. This approach not only preserves user privacy but also enhances user engagement by providing actionable insights based on local consumption patterns.