Using intelligent data analysis to detect abnormal energy consumption in buildings

This paper describes a novel method for detecting abnormal energy consumption in buildings based on daily readings of energy consumption and peak energy consumption. The method uses outlier detection to determine if the energy consumption for a particular day is significantly different than previous energy consumption. For buildings with abnormal energy consumption, the amount of variation from normal is determined using robust estimates of the mean and standard deviation. This new data analysis method will reduce operating costs by detecting problems that previously would have gone unnoticed. Also, operators should save time by not having to manually detect faults or diagnose false alarms. The new data analysis method has successfully detected high-energy consumption in many buildings. This paper presents field test results for buildings that had the following problems: (1) chiller failure and a poor control strategy, (2) poor design of ventilating and air-conditioning equipment, and (3) improper operation of equipment following a change in the electrical panel.

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