AMPds: A Public Dataset for Load Disaggregation and Eco-Feedback Research

被引：217

作者：

Makonin, Stephen ^{[1
]}

Popowich, Fred ^{[1
]}

Bartram, Lyn ^{[1
]}

Gill, Bob ^{[2
]}

Bajic, Ivan V. ^{[1
]}

机构：

[1] Simon Fraser Univ, Ctr Technol, Interact Arts & Technol, Comp Sci & Engn Sci, Burnaby, BC V5A 1S6, Canada

[2] British Columbia Inst Technol, Sch Energy, Burnaby, BC, Canada

来源：

2013 IEEE ELECTRICAL POWER & ENERGY CONFERENCE (EPEC) | 2013年

基金：

加拿大自然科学与工程研究理事会;

关键词：

Power Meter; Current; Dataset; Load Disaggregation; Eco-Feedback; Single-Measurement; Maximum a Posteriori (MAP); Energy Conservation; VISUALIZATION;

D O I：

10.1109/EPEC.2013.6802949

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

A home-based intelligent energy conservation system needs to know what appliances (or loads) are being used in the home and when they are being used in order to provide intelligent feedback or to make intelligent decisions. This analysis task is known as load disaggregation or non-intrusive load monitoring (NILM). The datasets used for NILM research generally contain real power readings, with the data often being too coarse for more sophisticated analysis algorithms, and often covering too short a time period. We present the Almanac of Minutely Power dataset (AMPds) for load disaggregation research; it contains one year of data that includes 11 measurements at one minute intervals for 21 sub-meters. AMPds also includes natural gas and water consumption data. Finally, we use AMPds to present findings from our own load disaggregation algorithm to show that current, rather than real power, is a more effective measure for NILM.

引用

页数：6

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