Journal
APPLIED SCIENCES-BASEL
Volume 11, Issue 2, Pages -Publisher
MDPI
DOI: 10.3390/app11020668
Keywords
HVAC system; multi-mode model; gap metric; SVM based clustering and regression
Categories
Funding
- Mitacs [IT18993]
Ask authors/readers for more resources
The study proposes a new multi-model approach to identify and regulate HVAC systems, which simultaneously conducts clustering and regression steps, optimizes cost functions, and develops a global model using a gap metric-based approach.
Heat, ventilation and air conditioning (HVAC) is a crucial system for maintaining acceptable air quality and keeping the building and its occupants healthy. There are some challenges in controlling and identifying this system as it commonly operates in different operation conditions. Furthermore, various types of un-controlled sources disturb the steady operations. In addition, an HVAC system is an inherently nonlinear system and varies with time. As a result, conventional methods are not successful in identifying and controlling this system. This paper proposes a new multi-model approach in which the clustering and regression steps are performed simultaneously to tackle this problem. Cost functions of clustering and regression steps are combined and optimized using an iterative algorithm. After identifying the local models, a gap metric based approach is used to develop a global model of the process. The proposed approach is tested on a simulated ventilation unit system and real-world dataset. The results show the performance of the proposed method of identifying the ventilation system.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available