IntEnergy: an efficient federated learning model for energy consumption forecasting in smart homes

The rapid progress of urbanization and the looming demands of 6G networks exacerbate energy consumption concerns, particularly in smart homes, where energy demand is increasingly difficult to predict and manage efficiently. Many existing studies on energy prediction in smart homes rely on limited and small-scale datasets, which restrict the robustness and generalizability of predictive models. To address these limitations, this paper introduces IntEnergy, a novel Federated Learning (FL) model designed to optimize energy efficiency in the 6G era. IntEnergy employs a regional allocation strategy where each region, consisting of multiple smart homes, assigns fog nodes to handle preprocessed data, enabling horizontal offloading between adjacent fog nodes for improved local model convergence. Utilising the portable XGBoost library in conjunction with the Flower framework, it creates a two-phase decentralized FL setup to simplify model complexity while enhancing scalability and robustness. The model leverages a comprehensive and diverse dataset to train local models in individual smart homes, ensuring improved generalizability of predictions. Experimental results demonstrate that IntEnergy surpasses state-of-the-art methods in terms of Normalized Root Mean Square Error (NRMSE) of 0.095, Mean Absolute Error (MAE) of 27.15 and R-squared value of 0.91, which highlight its efficacy in energy management in smart homes. By addressing existing limitations and introducing innovative strategies for decentralized processing and forecasting, IntEnergy provides more accurate and reliable predictions of energy consumption in the 6G era.