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RAE: The Rainforest Automation Energy Dataset for Smart Grid Meter Data Analysis Makonin, Stephen; Wang, Z. Jane (Zheng Jane); Tumpach, Chris

Abstract

Datasets are important for researchers to build models and test how well their machine learning algorithms perform. This paper presents the Rainforest Automation Energy (RAE) dataset to help smart grid researchers test their algorithms that make use of smart meter data. This initial release of RAE contains 1 Hz data (mains and sub-meters) from two residential houses. In addition to power data, environmental and sensor data from the house’s thermostat is included. Sub-meter data from one of the houses includes heat pump and rental suite captures, which is of interest to power utilities. We also show an energy breakdown of each house and show (by example) how RAE can be used to test non-intrusive load monitoring (NILM) algorithms.

Item Metadata

Title
RAE: The Rainforest Automation Energy Dataset for Smart Grid Meter Data Analysis
Creator
Makonin, Stephen; Wang, Z. Jane (Zheng Jane); Tumpach, Chris
Publisher
Multidisciplinary Digital Publishing Institute
Date Issued
2018-02-12
Description
Datasets are important for researchers to build models and test how well their machine learning algorithms perform. This paper presents the Rainforest Automation Energy (RAE) dataset to help smart grid researchers test their algorithms that make use of smart meter data. This initial release of RAE contains 1 Hz data (mains and sub-meters) from two residential houses. In addition to power data, environmental and sensor data from the house’s thermostat is included. Sub-meter data from one of the houses includes heat pump and rental suite captures, which is of interest to power utilities. We also show an energy breakdown of each house and show (by example) how RAE can be used to test non-intrusive load monitoring (NILM) algorithms.
Subject
Dataset; Residential; Smart grid; Smart meter; In-home display; IHD; HEMS; Non-intrusive load monitoring; Disaggregation; NILM
Genre
Article
Type
Text
Language
eng
Date Available
2019-07-04
Provider
Vancouver : University of British Columbia Library
Rights
CC BY 4.0
DOI
10.14288/1.0379750
URI
http://hdl.handle.net/2429/70890
Affiliation
Applied Science, Faculty of; Non UBC; Electrical and Computer Engineering, Department of
Citation
Data 3 (1): 8 (2018)
Publisher DOI
10.3390/data3010008
Peer Review Status
Reviewed
Scholarly Level
Faculty
Rights URI
https://creativecommons.org/licenses/by/4.0/
Aggregated Source Repository
DSpace

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CC BY 4.0