A Data-driven Fault Detection and Diagnostics Impact Analysis Framework for Enhancing Building Indoor Air Quality

<p> Abnormal operation of HVAC systems can lead to higher energy consumption, poor indoor air  quality, thermal discomfort, and reduced productivity. However, current HVAC fault detection  & diagnostics (FDD) metrics have been primarily developed for energy savings, with little  consideration of the impacts on indoor air quality (IAQ). This thesis proposes a data-driven fault  impact analysis framework for enhancing building IAQ. After a systematic review of fault rules  from three leading vendors, a curated list of faults that influence IAQ are selected. Based on two  years of time series BAS and FDD data for a federal office building, Machine Learning tools  help to quantify the relationship between building operation faults and indoor carbon dioxide  (CO2) levels, to help generate FDD priorities for IAQ. </p>