Very short-term chiller energy consumption prediction based on simplified heterogeneous graph convolutional network

Implementing predictive control within heating, ventilation, and air conditioning (HVAC) systems is imperative for maintaining operational efficacy and concurrently attaining energy efficiency. In the context of HVAC systems, accurate prediction of chiller energy consumption at very short-term, minute-level intervals is crucial for the timely implementation of optimal predictive control strategies. Current research predominantly focuses on short-term prediction at hourly or daily intervals, relying heavily on historical data for predictive insights. However, this approach imposes significant data acquisition burdens and may deviate from practical feasibility for very short-term prediction needs. This study introduces a novel approach employing the graph convolutional network (GCN) model for very short-term energy prediction based on an integrated simple graph (ISG-GCN) leveraging solely antecedent temporal information. The ISG addresses the challenges of determining connection weights between interdependent parameters, thereby eliminating the need for trial-and-error methods or reliance on potentially inaccurate historical correlation coefficients. Conceptualizing the chiller system as a black box, the model necessitates only seven discrete data points, significantly mitigating the data acquisition workload and facilitating seamless integration into buildings devoid of sophisticated sensor infrastructure. Applied to two simulation datasets encompassing large office buildings and one real operational dataset, the proposed model attains a mean absolute percentage error as minimal as 5.2%, demonstrating its effectiveness in real operational environments.