2013 HeatPumpDetectionfromCoarseGrai

• (Fei et al., 2013) ⇒ Hongliang Fei, Younghun Kim, Sambit Sahu, Milind Naphade, Sanjay K. Mamidipalli, and John Hutchinson. (2013). “Heat Pump Detection from Coarse Grained Smart Meter Data with Positive and Unlabeled Learning.” In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2174-7 doi:10.1145/2487575.2488203

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Cited By

• http://scholar.google.com/scholar?q=%222013%22+Heat+Pump+Detection+from+Coarse+Grained+Smart+Meter+Data+with+Positive+and+Unlabeled+Learning
• http://dl.acm.org/citation.cfm?id=2487575.2488203&preflayout=flat#citedby

Quotes

Author Keywords

• Data mining; feature extraction; heat pump detection; positive and unlabeled learning; smart meter data mining

Abstract

Recent advances in smart metering technology enable utility companies to have access to tremendous amount of smart meter data, from which the utility companies are eager to gain more insight about their customers. In this paper, we aim to detect electric heat pumps from coarse grained smart meter data for a heat pump marketing campaign. However, appliance detection is a challenging task, especially given a very low granularity and partial labeled even unlabeled data. Traditional methods install either a high granularity smart meter or sensors at every appliance, which is either too expensive or requires technical expertise. We propose a novel approach to detect heat pumps that utilizes low granularity smart meter data, prior sales data and weather data. In particular, motivated by the characteristics of heat pump consumption pattern, we extract novel features that are highly relevant to heat pump usage from smart meter data and weather data. Under the constraint that only a subset of heat pump users are available, we formalize the problem into a positive and unlabeled data classification and apply biased Support Vector Machine (BSVM) to our extracted features. Our empirical study on a real-world data set demonstrates the effectiveness of our method. Furthermore, our method has been deployed in a real-life setting where the partner electric company runs a targeted campaign for 292, 496 customers. Based on the initial feedback, our detection algorithm can successfully detect substantial number of non-heat pump users who were identified heat pump users with the prior algorithm the company had used.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 HeatPumpDetectionfromCoarseGrai	Hongliang Fei Sambit Sahu Milind Naphade Younghun Kim Sanjay K. Mamidipalli John Hutchinson			Heat Pump Detection from Coarse Grained Smart Meter Data with Positive and Unlabeled Learning				10.1145/2487575.2488203		2013