Abstract
The electrical grid is transitioning to new smart grid technology. With smart meters becoming an essential feature in smart homes, concerns regarding smart meters and the vast amount of consumer data that it captures are on the rise. While access to this fine-grained energy consumption data captured by smart meters can potentially violate consumer privacy, advanced analysis of this data can help to protect the interest of both the consumer and the utility company by enabling fraud detection at either end. The use of machine learning techniques has been a very common approach to energy fraud detection. Patterns in energy consumption can be recognized and used to detect anomalous behavior. This research reports on a novel application of decision tree learning technique to profile normal energy consumption behavior allowing for the detection of potentially fraudulent activity.
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