Energy efficient model based algorithm for control of building HVAC systems.

Area of Science:

• Engineering
• Control Systems
• Building Science

Background:

• Energy efficiency in building operations is a growing concern.
• Heating, Ventilation, and Air Conditioning (HVAC) systems are major energy consumers.
• Balancing energy usage with thermal comfort is crucial for HVAC control.

Purpose of the Study:

• To develop and evaluate an explicit Model Predictive Control (MPC) strategy for building HVAC systems.
• To improve energy efficiency and thermal comfort in buildings.
• To validate the control strategy using real-time data and hardware-in-loop testing.

Main Methods:

• Real-time data from a building HVAC system was utilized.
• A resistor-capacitor (RC) framework estimated the building's thermal dynamics via Particle Swarm Optimization (PSO).
• An explicit MPC was designed based on the state-space representation of the building's thermal dynamics.

Main Results:

• The explicit MPC strategy was implemented and tested on a PIC32mx microcontroller.
• Hardware-in-loop (HIL) testing was performed using MATLAB and a USB port.
• Compared to Proportional Integral (PI) controllers, explicit MPC demonstrated significant improvements in both energy efficiency and thermal comfort.

Conclusions:

• Explicit MPC offers a superior approach for HVAC control compared to traditional PI controllers.
• The developed strategy effectively balances energy savings with occupant thermal comfort.
• The study validates the practical implementation and effectiveness of explicit MPC for real-world building applications.