Abnormal energy consumption detection using ensemble model for water chilling unit on HVAC system.

To address the problem that the energy consumption of chiller is affected by multiple factors and lacking of abnormal detection model which can be used for energy consumption of chillers, an integrated model based on Partitioning around medoids(PAM), Support Vector Classification (SVC) and 3-σ rules is proposed in this study. In the detection of abnormal energy consumption of the chillers, the observed data of a Heating Ventilation and Air Conditioning (HVAC) system from Hubei province, China are used to develop and evaluate the integrated model. PAM is applied to divide consumption patterns of the historical data. SVC is applied to recognize consumption patterns of the testing data. 3-σ rules are used for abnormal detection. The results show that PAM divide the energy consumption into four patterns, SVC multi-classifier can effectively recognize energy consumption patterns with an accuracy reaches 98.29%. Compared with the popular model, the integrated learning model applied for the detection of abnormal energy consumption reaches a twice as high as the 40% detection success rate of the 3-σ rules. Moreover, the patterns recognition component can be used to understand the people's energy consumption behavior and the anomaly detection component can be used to regulate the energy usage. [ABSTRACT FROM AUTHOR]