Federated Learning-Enabled Energy Management in Smart Buildings for Sustainability

In the pursuit of sustainable energy usage, the implementation of an advanced energy management system (EnMS) holds immense promise. This study focuses on enhancing energy efficiency and personalization within smart buildings by integrating cutting-edge technologies. Specifically, we present a novel approach that leverages predictive models, such as the Markov two-state model, to achieve an impressive accuracy rate of 78% in forecasting the state of electrical appliances. This predictive accuracy is paired with a remarkable efficiency of 92%.