Set-point optimization frameworks for leveraging passive thermal storage in buildings

Reducing the operating energy costs of commercial buildings is an important problem faced by the facility managers. Additionally, energy cost from heating-ventilation-air-conditioners (HVAC) is a significant fraction (often greater than 50%) of the overall operational cost of these buildings. In this paper, we propose a pre-cooling framework that uses a “gray box” thermal model of a building to compute the optimal set-point temperature schedules to minimize the operational cost of the HVACs. We also propose a technique which involves relaxing the non-linear constraint to improve the computational complexity of the optimization framework. This optimization framework is tested using data obtained from the building management system (BMS) of a commercial building in Australia. The optimal scheduling of the set-point temperatures results in peak power reduction by over 40% and energy bills by 25% to 30%. Furthermore, the relaxation of the non-linearity reduces the computation time approximately by a factor of 50 while resulting in only a negligible loss in optimality of about 2%. Reduction in computation time of the optimization increases its amenability to use this framework alongside existing BMS systems.