Enhancing Energy Consumption Prediction in Smart Homes: A Convergence-Aware Federated Transfer Learning Approach

Abstract

Achieving accurate energy consumption prediction can be challenging, particularly in residential buildings, which experience highly variable consumption behavior due to changes in occupants and the construction of new buildings. This variability, combined with the potential for privacy breaches through conventional data collection methods, underscores the need for novel approaches to energy consumption forecasting. The proposed study suggests a new approach to predict energy consumption, utilizing Federated Learning (FL) to train a global model while ensuring local data privacy and transferring knowledge from information-rich to information-poor buildings. The proposed method learns the transferable knowledge from the source building without any privacy leakage and utilizes it for target buildings. Since the performance of the global model could be negatively affected by some participating nodes with poor performance due to noisy or limited data, we propose a client selection strategy on the server based on the normal distribution for choosing the best possible participants for the global model. Our method enables clients to participate selectively in the aggregation procedure to avoid model divergence due to poor performance. The proposed model is evaluated and conducts in-depth analyses of energy consumption patterns. We validate the performance by comparing its MAE, MSE, and R2 values to those of existing traditional and ensemble models. Our findings indicate that the proposed FL-based model with selective client participation outperforms its counterpart methods regarding predictive accuracy and robustness. The source code is available on Github