Fault Detection and Diagnosis of an HVAC system using Artificial Immune Recognition System

Heating, Ventilation, and Air Conditioning (HVAC) is essential to providing a comfortable indoor environment for the occupants in a building, while it also consumes a majority of the building energy. Operating with fault, the HVAC system can be more energy consuming and eventually lead to the degraded comfort experience of occupants. This paper is focused on applying an intelligent classification method, Artificial Immune Recognition System (AIRS), in solving the Fault Detection and Diagnosis (FDD) problem of an HVAC system simulated by Trnsys. The AIRS classifier is run on the WEKA classification tool. Thirteen fault types for a selected zone in a multi-zone building are considered in this study. To achieve more comprehensive results, the simulation is carried out for a whole year.