Multiserver Federated Learning for Building Energy Prediction With Wind Speed

Energy consumption of buildings is correlated with environmental factors such as weather and temperature. Developing a reliable energy forecasting model requires a large-scale, high-quality dataset. However, privacy regulations mandate that data be stored locally, prohibiting data sharing. As a solution, a Federated Building Energy Forecasting framework has emerged, supporting machine learning using decentralized energy data. This framework facilitates the communication of deep learning models between a server and multiple clients. However, it remains vulnerable to server crashes, which can disrupt the entire network’s functionality. To address this issue, multiserver federated learning strategies have been proposed to reduce dependency on a single server and enhance reliability. Additionally, the meteorological variable of wind speed has been integrated to improve energy forecasting through multitask learning (MTL). An MTL method is proposed to forecast energy load and wind speed simultaneously, employing a mixture-of-experts (MoE) architecture. Furthermore, a new metaformer neural network has been designed using the Taylor expansion approximation to simplify self-attention mechanisms. Extensive experiments have evaluated the effectiveness of MTL and multiserver federated training. The results demonstrate that forecasting accuracies have improved by 10%–30% compared to existing state-of-the-art transformer forecasting models. Additionally, incorporating wind speed in the MoE framework benefits energy forecasting. Multiserver strategies also help mitigate data scarcity issues through strategic random clustering.