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This study describes three different data mining techniques for detecting abnormal lighting energy consumption using
hourly recorded energy consumption and peak demand (maximum power) data. Two outliers’ detection methods are
applied to each class and cluster for detecting abnormal consumption in the same data set. In each class and cluster
with anomalous consumption the amount of variation from normal is determined using modified standard scores. The
study will be helpful for building energy management systems to reduce operating cost and time by not having to
detect faults manually or diagnose false warnings. In addition, it will be useful for developing fault detection and
diagnosis model for the whole building energy consumption
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