Multi-Model Identification of HVAC System

• Main Authors: Yousef Alipouri, Lexuan Zhong
• Format: Article
• Language: English
• Published: MDPI AG 2021-01-01
• Series: Applied Sciences
• Subjects: HVAC system; multi-mode model; gap metric; SVM based clustering and regression
• Online Access: https://www.mdpi.com/2076-3417/11/2/668

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.