An Ensemble Multi-Task Learning Model for Predictive Performance Evaluation of Air Handling Units in HVAC Systems

With urbanization resulting in increased demand for indoor comfort, HVAC (heating, ventilation, and air-conditioning) systems, particularly air handling units (AHUs), are essentials for indoor climate control. The advent of big data and artificial intelligence (AI) have opened new avenues for enhanced safety and reliability in HVAC operations. Hence, this study focused on the predictive performance evaluation of AHUs, which is receiving less attention compared to its fault detection and optimal control issues. Utilizing real-time operational data from Oak National Laboratory, the proposed model employs multi-task learning (MTL) to refine prediction accuracy for AHU return air properties, including temperature, moisture content, and power consumption. This is achieved without allowing any single task to dominate others during the training phase. Moreover, the model introduces an ensemble approach that synergizes the capabilities of the different MTL algorithms using a boosting technique via gradient boosting regression tree (GBRT). This novel strategy has demonstrated superiority over conventional data-driven approaches in terms of performance. The paper culminates by showcasing the significant role of the proposed model as a metric for AHU performance evaluation and its contribution to smart decision-making in a real-world context. In essence, the developed model is poised to facilitate optimal decision-making regarding HVAC components and foster proactive strategies to ensure consistent operation and extend the lifespan of HVAC systems.