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Intelligent Approaches for Modeling and Optimizing HVAC Systems’ Energy Use

[+] Author Affiliations
Raymond C. Tesiero, III, Balakrishna Gokaraju, Daniel Adrian Doss

University of West Alabama, Livingston, AL

Nabil Nassif

North Carolina A&T State University, Greensboro, NC

Paper No. ES2017-3105, pp. V001T09A002; 10 pages
doi:10.1115/ES2017-3105
From:
  • ASME 2017 11th International Conference on Energy Sustainability collocated with the ASME 2017 Power Conference Joint With ICOPE-17, the ASME 2017 15th International Conference on Fuel Cell Science, Engineering and Technology, and the ASME 2017 Nuclear Forum
  • ASME 2017 11th International Conference on Energy Sustainability
  • Charlotte, North Carolina, USA, June 26–30, 2017
  • Conference Sponsors: Advanced Energy Systems Division, Solar Energy Division
  • ISBN: 978-0-7918-5759-5
  • Copyright © 2017 by ASME

abstract

Advanced energy management control systems (EMCS), or building automation systems (BAS), offer an excellent means of reducing energy consumption in heating, ventilating, and air conditioning (HVAC) systems while maintaining and improving indoor environmental conditions. This can be achieved through the use of computational intelligence and optimization. This paper evaluates model-based optimization processes (OP) for HVAC systems utilizing any computer algebra system (CAS), genetic algorithms and self-learning or self-tuning models (STM), which minimizes the error between measured and predicted performance data. The OP can be integrated into the EMCS to perform several intelligent functions achieving optimal system performance. The development of several self-learning HVAC models and optimizing the process (minimizing energy use) is tested using data collected from an actual HVAC system.

Using this optimization process (OP), the optimal variable set points (OVSP), such as supply air temperature (Ts), supply duct static pressure (Ps), chilled water supply temperature (Tw), minimum outdoor ventilation, and chilled water differential pressure set-point (Dpw) are optimized with respect to energy use of the HVAC’s cooling side including the chiller, pump, and fan. The optimized set point variables minimize energy use and maintain thermal comfort incorporating ASHRAE’s new ventilation standard 62.1-2013.

This research focuses primarily with: on-line, self-tuning, optimization process (OLSTOP); HVAC design principles; and control strategies within a building automation system (BAS) controller. The HVAC controller will achieve the lowest energy consumption of the cooling side while maintaining occupant comfort by performing and prioritizing the appropriate actions. The program’s algorithms analyze multiple variables (humidity, pressure, temperature, CO2, etc.) simultaneously at key locations throughout the HVAC system (pumps, cooling coil, chiller, fan, etc.) to reach the function’s objective, which is the lowest energy consumption while maintaining occupancy comfort.

Copyright © 2017 by ASME

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