Application of computational intelligence in modeling and optimization of HVAC systems

HVAC (Heating Ventilating and Air-Conditioning) system is multivariate, nonlinear, and shares time-varying characteristics. It poses challenges for both system modeling and performance optimization. Traditional modeling approaches based on mathematical equations limit the nature of the optimization models and solution approaches.

Computational intelligence is an emerging area of study which provides powerful tools for modeling and analyzing complex systems. Computational intelligence is concerned with discovery of structures in data and recognition of patterns. It encompasses techniques such as neural networks, fuzzy logic, and so on. These techniques derive rules, patterns, and develop complex mappings from the data. The recent advances in information technology have enabled collection of large volumes of data. Computational intelligence embraces biology-inspired paradigms like evolutionary computation and particle swarm intelligence in solving complex optimization problems.

Successful applications of computational intelligence have been found in business, marketing, medical and manufacturing domains. The focus of this thesis is to apply computational intelligence approach in modeling and optimization of HVAC systems. In this research, four HVAC sub-systems are investigated: the AHU (Air Handling Unit), VAV (Variable Air Volume), ventilation system, and thermal zone. Various computational intelligence approaches are used to identify parameters or problem solving. Energy savings are accomplished by minimizing the cooling output, reheating output or fan running time as well as on-line monitoring. One contribution of the research reported in the thesis is the use of computational intelligence algorithms to establish nonlinear mappings among different parameters. Another major contribution is in using heuristics algorithms to solve multi-objective optimization problems.