Date of Award

2014

Document Type

Thesis

First Advisor

Nassif, Nabil

Abstract

Heating, ventilation, and air-conditioning account for a vast majority of energy consumption in the residential and commercial sectors. Intelligent energy management control system (EMCS) in buildings offers an excellent means of reducing energy consumption in heating, ventilation, and air-conditioning (HVAC) systems while maintaining or improving indoor environmental conditions. This can be achieved through the use of computational intelligence and optimization. This project proposes and evaluates a model-based optimization process for HVAC systems using an evolutionary algorithm. The process can be integrated into the EMCS to perform several intelligent functions and achieve optimal whole-system performance. The proposed process addresses the requirements of the latest ASHRAE Standard 62.1. A whole building simulation energy software is used to generate the sub hourly load. The simulations are performed to test the process and determine the potential energy savings achieved. In addition, simulations were conducted at peak load on July 15th and partial load on April 10th to observe the effects of genetic algorithm (GA). Through artificial intelligence utilization, the energy consumption can be better managed. Building controls are like living organisms which can be treated much like evolutionary biology during programming. The single-objective GA optimization and modernized ventilation codes have demonstrated that total energy consumed by the HVAC system can be reduced by 30.6% for the air side distribution.

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