Simulation and optimal control of heating and cooling systems: A case study of a commercial building

Abstract

This paper proposes a novel energy conservation measure that optimizes the planning of heating and cooling systems for tertiary sector buildings. It consists of a model-based predictive control approach that employs a grey-box model built from the building and weather data that predicts the building heat load and indoor temperature. Different from classical optimization approaches where the discretized differential algebraic equations are integrated into the optimization formulation, our model is calibrated using black-box multiobjective optimization, which allows for decoupling the predictive model from the optimization problem, thus having more flexibility and reducing the total computational time. Moreover, rather than requiring the angle of solar radiation, solar orientation and solar masks to calculate the radiation data, our approach requires only a simple model of the solar irradiance. The calibrated model is then used by heating and cooling optimization strategies that aim at reducing the energy consumption of the building in the next day while satisfying the indoor thermal constraints. The proposed approach was applied in a case study of a commercial building during heating and cooling seasons and the results show that it was able to yield up to 12% of energy savings while having a mean power forecast error of 8%.