Theory and applications of HVAC control systems – A review of model predictive control (MPC)

Buildings all around the world consume a significant amount of energy, which is more or less one-third of the total primary energy resources. This has raised concerns over energy supplies, rapid energy resource depletion, rising building service demands, improved comfort life styles along with the increased time spent in buildings; consequently, this has shown a rising energy demand in the near future. However, contemporary buildings’ energy efficiency has been fast tracked solution to cope/limit the rising energy demand of this sector. Building energy efficiency has turned out to be a multi-faceted problem, when provided with the limitation for the satisfaction of the indoor comfort index. However, the comfort level for occupants and their behavior have a significant effect on the energy consumption pattern. It is generally perceived that energy unaware activities can also add one-third to the building’s energy performance. Researchers and investigators have been working with this issue for over a decade; yet it remains a challenge. This review paper presents a comprehensive and significant research conducted on state-of-the-art intelligent control systems for energy and comfort management in smart energy buildings (SEB’s). It also aims at providing a building research community for better understanding and up-to-date knowledge for energy and comfort related trends and future directions. The main table summarizes 121 works closely related to the mentioned issue. Key areas focused on include comfort parameters, control systems, intelligent computational methods, simulation tools, occupants’ behavior and preferences, building types, supply source considerations and countries research interest in this sector. Trends for future developments and existing research in this area have been broadly studied and depicted in a graphical layout. In addition, prospective future advancements and gaps have also been discussed comprehensively.

A review on optimized control systems for building energy and comfort management of smart sustainable buildings

https://escholarship.org/content/qt5rh8s2hc/qt5rh8s2hc.pdf?t=qaoodo

Reinforcement learning for building controls: The opportunities and challenges

Optimal control of HVAC and window systems for natural ventilation through reinforcement learning

Design optimization and optimal control of grid-connected and standalone nearly/net zero energy buildings

Multiple-zone ventilation and temperature control of a single-duct VAV system using model predictive strategy

This paper presents an integrated approach to controlling dry-bulb supply air temperature and outside air intake rate of an air-handling unit (AHU) by adopting a model predictive control strategy. The dynamics of the AHU are modeled with a multi-input-multi-output (MIMO) model that has a linear structure andtime-variant gains. The model predictive control (MPC) controller can respond properly to different working conditions by assigning different weighting factors and constraint limits to a convex quadratic optimization problem. Weighting factors and constraint limits can be tuned in an intuitive way and their control effects are easy to understand. A building simulator was created based on the first-principle component models developed in ASHRAE research project RP-825. The simulation-based experiments demonstrated that good temperature and flow rate controls were both achieved by using MPC.

Shui Yuan

Papers

Multiple-zone ventilation and temperature control of a single-duct VAV system using model predictive strategy

Model predictive control of supply air temperature and outside air intake rate of a VAV air-handling unit