Energy modeling and control for a thermostatically controlled load (TCL) using intelligent optimization algorithms

Abstract

In this research work, we proposed an on-and-off parameter identification methodology for establishing a single-zone TCL model. A case study was conducted to validate the proposed model. A temperature prediction technique was also developed for a single-zone Heating, Ventilation and Air Conditioning (HVAC) system. We also conducted another case study in the Wu Conference Centre at the University of New Brunswick (UNB) to validate this technique. A Multi-Objective Genetic Algorithm (MOGA)-based intelligent control strategy for load shifting and energy conservation was then developed. It integrated the occupancy information, Time-of-Use (ToU) pricing, and the temperature prediction technique into the optimization process, and used the single-zone TCL model as a testbed to evaluate the control performance. All simulations of this research work were built and tested in MATLAB® R2017a.