Structured Dictionary Learning for Energy Disaggregation
Structured Dictionary Learning for Energy Disaggregation
复制标题
用于能源分解的结构化字典学习
DOI：
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复制
发表时间：
2019
期刊：
影响因子：
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通讯作者：
G. Karypis
中科院分区：
文献类型：
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作者：
Shalini Pandey;G. Karypis
人们对能源消耗对环境的影响的认识越来越多，导致人们对减少能源消耗的关注越来越大。对设备级别能耗的反馈可以帮助减少消费者的能源需求。能源分解技术用于从房屋的总能源消耗中获得设备水平的能量消耗。这些技术将单个设备的能源消耗提取为特征，因此面临着区分两个相似能源消耗设备的挑战。为了应对这一挑战，我们开发的方法利用了某些设备倾向于在特定操作模式下同时运行的事实。设备亚组的汇总能耗模式使我们能够识别子组中设备的并发操作模式。因此，我们设计了分层方法，以替换设备之间整体能量分解的任务，其中涉及设备子组的递归分解任务。两个现实世界数据集的实验表明，与基线相比，我们的方法可提高性能。我们的一种方法之一，基于贪婪的设备分解方法（GDDM）的方法分别提高了23.8％，10％和59.3％，以微平均F分数，宏观平均F分数和归一化的分解误差（NDE）提高。
The increased awareness regarding the impact of energy consumption on the environment has led to an increased focus on reducing energy consumption. Feedback on the appliance level energy consumption can help in reducing the energy demands of the consumers. Energy disaggregation techniques are used to obtain the appliance level energy consumption from the aggregated energy consumption of a house. These techniques extract the energy consumption of an individual appliance as features and hence face the challenge of distinguishing two similar energy consuming devices. To address this challenge we develop methods that leverage the fact that some devices tend to operate concurrently at specific operation modes. The aggregated energy consumption patterns of a subgroup of devices allows us to identify the concurrent operating modes of devices in the subgroup. Thus, we design hierarchical methods to replace the task of overall energy disaggregation among the devices with a recursive disaggregation task involving device subgroups. Experiments on two real-world datasets show that our methods lead to improved performance as compared to baseline. One of our approaches, Greedy based Device Decomposition Method (GDDM) achieved up to 23.8%, 10% and 59.3% improvement in terms of micro-averaged f score, macro-averaged f score and Normalized Disaggregation Error (NDE), respectively.
DOI：
10.1609/aaai.v31i1.11179
发表时间：
2017-02
期刊：
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影响因子：
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作者：
Nipun Batra;Hongning Wang;Amarjeet Singh;K. Whitehouse
通讯作者：
Nipun Batra;Hongning Wang;Amarjeet Singh;K. Whitehouse
使用神经网络进行序列到点学习，用于非侵入式负载监控
DOI：
10.48550/arxiv.1612.09106
发表时间：
2016
期刊：
arXiv e-prints
影响因子：
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作者：
Zhang Chaoyun
通讯作者：
Zhang Chaoyun