Proceedings ArticleDOI
AMPds: A public dataset for load disaggregation and eco-feedback research
Stephen Makonin,Fred Popowich,Lyn Bartram,Bob Gill,Ivan V. Bajic +4 more
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TLDR
The Almanac of Minutely Power dataset (AMPds) is presented for load disaggregation research; it contains one year of data that includes 11 measurements at one minute intervals for 21 sub-meters, and also includes natural gas and water consumption data.Abstract:
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.read more
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Journal ArticleDOI
The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes
TL;DR: This work presents UK-DALE: an open-access dataset from the UK recording Domestic Appliance-Level Electricity at a sample rate of 16 kHz for the whole-house and at 1/6 Hz for individual appliances, which is the first open access UK dataset at this temporal resolution.
Proceedings ArticleDOI
NILMTK: an open source toolkit for non-intrusive load monitoring
Nipun Batra,John Kelly,Oliver Parson,Haimonti Dutta,William J. Knottenbelt,Alex Rogers,Amarjeet Singh,Mani Srivastava +7 more
TL;DR: This work is the first research to compare multiple disaggregation approaches across multiple publicly available data sets, and demonstrates the range of reproducible analyses made possible by the toolkit.
Proceedings ArticleDOI
Matrix Profile I: All Pairs Similarity Joins for Time Series: A Unifying View That Includes Motifs, Discords and Shapelets
Chin-Chia Michael Yeh,Yan Zhu,Liudmila Ulanova,Nurjahan Begum,Yifei Ding,Hoang Anh Dau,Diego Furtado Silva,Abdullah Mueen,Eamonn Keogh +8 more
TL;DR: A novel scalable algorithm for time series subsequence all-pairs-similarity-search that computes the answer to the time series motif and time series discord problem as a side-effect, and incidentally provides the fastest known algorithm for both these extensively-studied problems.
Journal ArticleDOI
Short-Term Residential Load Forecasting Based on Resident Behaviour Learning
TL;DR: In this article, a long short-term memory-based deep-learning forecasting framework with appliance consumption sequences is proposed to address the volatile problem in residential load forecasting, which can be notably improved by including appliance measurements in the training data.
Journal ArticleDOI
The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes.
TL;DR: UK-DALE as mentioned in this paper is an open-access dataset from the UK recording Domestic Appliance-Level Electricity at a sample rate of 16 kHz for the whole house and at 1/6 kHz for individual appliances.
References
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Journal ArticleDOI
Nonintrusive appliance load monitoring
TL;DR: In this paper, a nonintrusive appliance load monitor that determines the energy consumption of individual appliances turning on and off in an electric load, based on detailed analysis of the current and voltage of the total load, as measured at the interface to the power source is described.
Journal ArticleDOI
Nonintrusive appliance load monitoring: Review and outlook
Michael I. Zeifman,Kurt Roth +1 more
TL;DR: This paper reviews algorithmic principles of consumer systems for NIALM in residential buildings in Residential buildings based on nonintrusive appliance load monitoring (NIALM).
Proceedings ArticleDOI
The design of eco-feedback technology
TL;DR: A comparative survey of eco-feedback technology is conducted, including 89 papers from environmental psychology and 44 papers from the HCI and UbiComp literature, to provide an overview of predominant models of proenvironmental behaviors and a summary of key motivation techniques to promote this behavior.
Proceedings Article
Unsupervised disaggregation of low frequency power measurements
TL;DR: This work investigates the effectiveness of several unsupervised disaggregation methods on low frequency power measurements collected in real homes and indicates that a conditional factorial hidden semi-Markov model, which integrates additional features related to when and how appliances are used in the home and more accurately represents the power use of individual appliances, outperforms the other unsuper supervision methods.
Proceedings Article
Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation
J. Zico Kolter,Tommi S. Jaakkola +1 more
TL;DR: This paper proposes an alternative inference method for additive factorial hidden Markov models, an extension to HMMs where the state factors into multiple independent chains, and the output is an additive function of all the hidden states.