Optimization Strategy for Residential Heating Based on Model Predictive Control

Abstract

Heating energy consumption accounts for a large proportion of energy use in residential buildings, and is a major problem in building energy efficiency; in addition, occupant demand for thermal comfort increases heating energy consumption and peak loads, further affecting total energy consumption. In this study, an optimization strategy based on model predictive control is proposed to optimize the occupants’ thermal comfort and heating energy consumption as the optimization objectives. Moreover, the study also introduces photovoltaic generation to consider the impact of the presence of photovoltaic on indoor thermal comfort and heating energy consumption. The proposed controller considers the outdoor environmental disturbances, and its control performance is tested by simulation on two independent residences and compared with the existing baseline control methods for dwellings. The results show that the optimization strategy based on model predictive control improves the fluctuation of room temperature, increases thermal comfort, reduces heating energy consumption and increases the PV consumption.