Cost-based Design Optimization for Air-Conditioning Systems in Commercial Green Buildings Using Artificial Immune Algorithm
Date Issued
2015
Date
2015
Author(s)
Su, Ching-Ya
Abstract
The electric power consumption of the air-conditioning system occupies a large proportion of the total electric power consumption in buildings, and the cost related to air-conditioning is also a large sum. However, a good selection of air-conditioning system with appropriate energy-saving technologies can help achieve maximum energy saving. In order to consider the energy-saving effect and the cost of air-conditioning system at the same time, this research aims to develop a smart system for air-conditioning design optimization for commercial green buildings. The artificial immune algorithm (AIA) is used in the design optimization system, which takes account of the Taiwanese green building standards, EEWH, and the minimization of energy consumption and costs. For the cost of the air-conditioning system, both the equipment cost and the energy consumption costs in the first 15 years of operation are considered. To validate the efficiency and effectiveness of the artificial immune algorithm (AIA), the results of AIA were compared to the enumerated results. The comparison results showed that the result difference between AIA and enumeration was merely 1.06%, while enumeration took more than 30,000 times the computation time of AIA. Besides, the calculated energy consumption result from the proposed system was compared to that of eQUEST, and the result difference fell well within 10% and was deemed acceptable. Through this optimization system, the user can choose the air-conditioning system and the expected EAC value they want, and then get the best combination of the equipment which meet the green building guidelines and spend the optimal cost. This solution can be taken as reference at the design phase.
Subjects
Green building
Air-conditiong system
Energy saving of air-conditioning system
Artificial immune algorithm (AIA)
SDGs
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-104-R02521705-1.pdf
Size
23.32 KB
Format
Adobe PDF
Checksum
(MD5):618be18bc16c1450d6f35e179236a162