Loading...

Model Predictive Control for Determining Optimal Real-time Operation of Heating (Cooling) System with Minimum Energy Consumption

Erfani Beyzaee, Arash | 2017

758 Viewed
  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 50123 (46)
  4. University: Sharif University of Technology
  5. Department: Energy Engineering
  6. Advisor(s): Rajabi-Ghahnavieh, Abbas; Boroushaki, Mehrdad
  7. Abstract:
  8. Considering the increasing importance of energy conservation in recent decades, different technologies regarding energy system have been developed to satisfy this goal. Building section is responsible for a big portion of final energy consumption and greenhouse gasses emission around the globe. Utilizing energy consumption reduction techniques in the building sector is among the foremost solutions to achieve the energy conservation potentials in this sector which would be followed by a decline in greenhouse emissions. Considering the contribution of heating and coling systems in a building’s energy use, optimizing these systems could have a huge effect on a building’s final energy consumption. The aim of this research is to optimize an air handling unit’s cooling system, which is located in faculty of energy engineering at Sharif University of Technology, based on the model predictive method. Since the case study is a multi-zone system, this research is more complicated than the ones conducted before, and it also requires a good understanding of the predictive control method and a deep insight in the control systems engineering. Predictive control is among the most efficient control approaches currently utilized for building’s HVAC systems. It needs measurements of the system’s parameters in order to predict system’s dynamic, it also considers system’s constraints and determines system’s controlled dynamic by minimizing an objective function as well. To model the plant’s thermal behavior, an artificial neural network model is applied. Model’s results demonstrate high efficiency and accuracy. Then, a model predictive controller has been designed along with specifying its parameters. The objective function is a weighted combination of gas and electricity energy carriers’ consumption and level of transgressing the thermal comfort of the occupants. Genetic algorithm is applied to solve the optimization problem during control horizon. Finally, results of implementing the predictive controller are presented. Results show a 55.1% reduction in electricity consumption and 43.7% reduction in gas consumption of the related AHU. It should be taken into notice that this tremendous reduction in energy use is achieved while maintaining the thermal comfort of the subjected zones during the office hours
  9. Keywords:
  10. Model Predictive Control ; Building Thermal Modeling ; Artificial Neural Network ; Energy Consumption Optimization ; Heat Control System ; Cooling System ; Multizone Heating, Ventilation and Air Conditioning (HVAC) Systems Control

 Digital Object List

 Bookmark

No TOC