Co-Design of Commercial Building HVAC using Bayesian Optimization

Sequential approaches to system and control design produce sub-optimal solutions due to unidirectional coupling between the system and control variables, i.e., the system design prescribes the control approach but not vice versa. A critical challenge to co-design is the complex coupling between heterogeneous variables: the time-independent system design variables and the time-varying control parameters. Traditional optimization-based co-design methods are unsuitable for large-scale engineering systems where the plant design is complex and accurate control-oriented, white-box models may not be available. These challenges have led to the widespread adoption of industry guidelines that often oversize commercial HVAC systems by 25–50% to satisfy conservative estimates of building peak-loads and redundancy margins. Significant energy, peak-demand, and capital cost savings can be realized with properly sized HVAC units and optimized control operation. In this paper, we develop a data-driven, black-box, co-design framework using Bayesian Optimization (BO) to co-optimize the system and control design variables for commercial building HVAC system. Using real chiller data, a detailed economic assessment and numerical study is conducted to illustrate the energy, peakload and capital cost savings against existing baseline strategies for chiller sizing and plant configuration.