Multi-task deep learning for large-scale buildings energy management

Abstract

Building energy management acts as the brain of the building, which controls the energy supply based on sensor data and algorithms. However, existing methods only focus on single-task prediction like load forecasting. As more multi-variable data is collected from ubiquitous sensors, building energy management needs to extend functionality from single-task to multi-purpose predictions. This study designs a multi-task learning system to tackle four different tasks: 1. Electricity load forecasting; 2. Air temperature forecasting; 3. Energy anomaly detection; 4. Energy anomaly prediction. A mixture-of-experts framework with the self-attention mechanism is proposed for learning heterogeneous tasks. A new comprehensive dataset has been created with real data to demonstrate the heterogeneous tasks' efficacy of the suggested framework. Extensive experiments are conducted with various deep learning models, which shows our proposed model achieves superior prediction performance overall tasks. Comparative studies are performed to explore the correlations between forecasting and anomaly learning, which reveal the benefits of multi-task learning for heterogeneous tasks. Anomaly detection and prediction both achieve 98% accuracy and 95% F1-score, while the electricity load forecasting single-task error is reduced by almost 60% through the multi-task model. Nonetheless, the tasks' training difficulties and resource consumption are also investigated and the deeper network doesn't ensure better performances. The dataset is open-sourced at: https://github.com/rekingbc/Multi-task-building.