Conference Editor
Jianshun Zhang; Edward Bogucz; Cliff Davidson; Elizabeth Krietmeyer
Location
Syracuse, NY
Event Website
http://ibpc2018.org/
Start Date
25-9-2018 3:15 PM
End Date
25-9-2018 5:00 PM
Description
Optimization algorithms plays a vital role in the Building Energy Optimization (BEO) technique. Although many algorithms are currently used in BEO, it is difficult to find an algorithm that performs well for all optimization problems. Some algorithms may fail in some cases. This study specifically focuses on failure algorithms in BEO and the possible causes. Several criteria are proposed for identifying failure algorithms. Four optimization problems base d on the DOE small and large office buildings are developed. Three commonly used algorithms in BEO, namely, Pattern Search (PS ) algorithm, Genetic Algorithm (GA ) and Particle Swarm Optimization (PSO) algorithm, are applied to the four problems to investigate possible rea sons for their failure. Results indicate that the effectiveness of the three selected algorithms is highly dependent on the optimization problems to be addressed. Besides, the control parameter setting of the PS algorithm appears to be a significant factor that may cause the algorithm to lose effectiveness. However, it does not seem to be the main reason for the failure of the GA and PSO algorithm. In General, the results gained from this study can deepen our understanding of optimization algorithms used in BEO. Besides, understanding the reasons why optimization algorithms are ineffective can help architects, engineers, and consultants select the appropriate optimization algorithms and set their parameters to achieve a better BEO design that is less vulnerable to failure.
Recommended Citation
Si, Binghui and Shi, Xing, "Criteria for identifying failure optimization algorithms in building energy optimization and case studies" (2018). International Building Physics Conference 2018. 13.
DOI
https://doi.org/10.14305/ibpc.2018.ps13
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Criteria for identifying failure optimization algorithms in building energy optimization and case studies
Syracuse, NY
Optimization algorithms plays a vital role in the Building Energy Optimization (BEO) technique. Although many algorithms are currently used in BEO, it is difficult to find an algorithm that performs well for all optimization problems. Some algorithms may fail in some cases. This study specifically focuses on failure algorithms in BEO and the possible causes. Several criteria are proposed for identifying failure algorithms. Four optimization problems base d on the DOE small and large office buildings are developed. Three commonly used algorithms in BEO, namely, Pattern Search (PS ) algorithm, Genetic Algorithm (GA ) and Particle Swarm Optimization (PSO) algorithm, are applied to the four problems to investigate possible rea sons for their failure. Results indicate that the effectiveness of the three selected algorithms is highly dependent on the optimization problems to be addressed. Besides, the control parameter setting of the PS algorithm appears to be a significant factor that may cause the algorithm to lose effectiveness. However, it does not seem to be the main reason for the failure of the GA and PSO algorithm. In General, the results gained from this study can deepen our understanding of optimization algorithms used in BEO. Besides, understanding the reasons why optimization algorithms are ineffective can help architects, engineers, and consultants select the appropriate optimization algorithms and set their parameters to achieve a better BEO design that is less vulnerable to failure.
https://surface.syr.edu/ibpc/2018/posters/13
Comments
If you are experiencing accessibility issues with this item, please contact the Accessibility and Inclusion Librarian through lib-accessibility@syr.edu with your name, SU NetID, the SURFACE link, title of record, and author & and reason for request.