Co-optimize condenser water temperature and cooling tower fan using high-fidelity synthetic data

Abstract

This paper introduces a novel method for optimizing HVAC systems in buildings by integrating a high-fidelity physics-based simulation model with machine learning and measured data. The method enables a real-time building advisory system that provides optimized settings for condenser water loop operation, assisting building operators in decision-making. The building and its HVAC system are first modeled using eQuest. Synthetic data are then generated by running the simulation multiple times. The data are then processed, cleaned, and used to train the machine learning model. The machine learning model enables real-time optimization of the condenser water loop using particle swarm optimization. The results deliver both a real-time online optimizer and an offline operation look-up table, providing optimized condenser water temperature settings and the optimal number of cooling tower fans at a given cooling load. Potential savings are calculated by comparing measured data from two summer months with the energy costs the building would have experienced under optimized settings. Adaptive model refinement is applied to further improve accuracy and effectiveness by utilizing available measured data. The method bridges the gap between simulation and real-time control. It has the potential to be applied to other building systems, including the chilled water loop, heating systems, ventilation systems, and other related processes. Combining physics models, data models, and measured data also enables performance analysis, tracking, and retrofit recommendations.