Enhanced Energy Efficiency and Adaptive Indoor Temperature Management for Residential Buildings Using Advanced Model Predictive Control Strategy

Abstract

In addressing the imperative need to optimize heating, ventilation, and air-conditioning (HVAC) systems for improved indoor comfort and reduced energy expenditures within buildings, Model Predictive Control (MPC) emerges as a highly effective algorithm for the proactive management of intricate HVAC systems. This article introduces an MPC model designed with the dual objectives of guaranteeing thermal comfort and minimizing energy consumption in residential heating contexts. The model, developed and simulated using the MATLAB Simulink platform, achieves these objectives through meticulous adjustments of MPC parameters, ensuring optimal heating energy consumption while sustaining comfort levels. Compared to a traditional PID controller, the proposed MPC demonstrated superior energy efficiency, achieving up to 22.7% in energy savings. These findings underscore the promising potential of MPC for intelligent management of residential heating systems, striking a balance between comfort and energy efficiency. The results highlight the practical advantages of adopting MPC in residential settings, paving the way for more sustainable and comfortable living environments.