Abstract
With the increasing use of heating, ventilating, and air conditioning (HVAC) systems nowadays, their energy consumption is receiving more attention. The study begins by trying available anomaly detection techniques, including KNN, COF, and isolated forests. The comparison reveals that these methods disregard some linear correlation results. Then, the pattern is summarized by analyzing data from 100 HVAC-equipped rooms. Next, the study uses correlation analysis and neural networks to identify abnormal HVAC data. Finally, it concludes by analyzing the factors that lead to the anomalies.
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