Design and construction of a non-linear model predictive controller for building's cooling system

Abstract

This research aims to optimize a multi-zone Air Handling Unit's (AHU) energy consumption by using a Non-linear Model Predictive Control (NMPC) approach. In this paper, Genetic Algorithm (GA) and Non-linear autoregressive network with exogenous inputs (NARX) have been utilized to design NMPC for a multi-zone AHU. The NMPC problem could be divided into two main sections: internal model and the optimizer. NARX serves as the controller's internal model to predict the building's thermal dynamics. GA is then used to solve the NMPC problem and find the optimal value of the control signals at each time step. The proposed NMPC jointly minimizes energy consumption of the AHU and the deviation from the set-point temperature. Finally, the designed controller was implemented and applied to the mentioned AHU. Also, a data acquisition system has been fabricated to secure training and test data for NARX. Utilizing NARX for modeling system's dynamics resulted in a highly accurate model with an accuracy of 97.71%. The empirical results of the proposed NMPC showed significant reduction in gas and electricity consumption of the AHU. NMPC yielded a 55.1% and 43.7% reduction in electricity and gas consumption of the AHU respectively.