Minimization of energy consumption in HVAC systems with data-driven models and an interior-point method

In this paper, a data-driven approach is applied to minimize energy consumption of a heating, ventilating, and air conditioning (HVAC) system while maintaining the thermal comfort of a building with uncertain occupancy level. The uncertainty of arrival and departure rate of occupants is modeled by the Poisson and uniform distributions, respectively. The internal heating gain is calculated from the stochastic process of the building occupancy. Based on the observed and simulated data, a multilayer perceptron algorithm is employed to model and simulate the HVAC system. The data-driven models accurately predict future performance of the HVAC system based on the control settings and the observed historical information. An optimization model is formulated and solved with the interior-point method. The optimization results are compared with the results produced by the simulation models.