A data driven pre-cooling framework for energy cost optimization in commercial buildings

Abstract

Commercial buildings consume significant amount of energy. Facility managers are increasingly grappling with the problem of reducing their buildings' peak power, overall energy consumption and energy bills. In this paper, we first develop an optimization framework - based on a gray box model for zone thermal dynamics - to determine a pre-cooling strategy that simultaneously shifts the peak power to low energy tariff regimes, and reduces both the peak power and overall energy consumption by exploiting the flexibility in a building's thermal comfort range. We then evaluate the efficacy of the pre-cooling optimization framework by applying it to buildingmanagement system(BMS) data, spanning several days, obtained froma large commercial building located in northern Australia. The results from simulations show that optimal pre-cooling reduces peak power by over 50%, energy consumption by up to 30% and energy bills by up to 37%. Next, to enable ease of use of our framework, we also propose a shortest path based heuristic algorithm for solving the optimization problem and show that it has comparable performance with the optimal solution. Finally, we describe an application of the proposed optimization framework for developing countries to reduce the dependency on expensive fossil fuels, which are often used as a source for energy backup. We conclude by highlighting our real world deployment of the optimal pre-cooling framework on the IBMBluemix cloud.Our pre-cooling methodology, based on the gray box optimization framework, incurs no capital expense and relies on data readily available from a BMS, thus enabling facility managers to take informed decisions for improving the energy and cost footprints of their buildings.