Heat pump detection from coarse grained smart meter data with positive and unlabeled learning

Hongliang Fei, Younghun Kim, S. Sahu, M. Naphade, Sanjay K. Mamidipalli, John Hutchinson
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引用次数: 34

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.
热泵检测从粗粒度智能电表数据与积极的和未标记的学习
智能电表技术的最新进展使公用事业公司能够访问大量的智能电表数据,公用事业公司渴望从中获得更多关于客户的洞察。在本文中,我们的目标是从热泵营销活动的粗粒度智能电表数据中检测电动热泵。然而,设备检测是一项具有挑战性的任务,特别是考虑到非常低的粒度和部分标记甚至未标记的数据。传统方法在每个设备上安装高粒度智能仪表或传感器,这要么太昂贵,要么需要技术专长。我们提出了一种新的方法来检测热泵,利用低粒度智能仪表数据,之前的销售数据和天气数据。特别是,受热泵消费模式特征的驱动,我们从智能电表数据和天气数据中提取与热泵使用高度相关的新特征。在只有一部分热泵用户可用的约束下,我们将问题形式化为一个正的和未标记的数据分类,并对我们提取的特征应用有偏支持向量机(BSVM)。我们对真实世界数据集的实证研究证明了我们方法的有效性。此外,我们的方法已经在现实环境中得到了应用,合作电力公司为292,496名客户开展了有针对性的活动。基于最初的反馈,我们的检测算法可以成功地检测出大量非热泵用户,这些用户通过公司之前使用的算法被识别为热泵用户。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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