RAE: The Rainforest Automation Energy Dataset for Smart Grid Meter Data Analysis

Stephen Makonin, Z. Jane Wang and Chris Tumpach
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Stephen Makonin: Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
Z. Jane Wang: Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
Chris Tumpach: Rainforest Automation, Inc., Burnaby, BC V5G 4P5, Canada

Data, 2018, vol. 3, issue 1, 1-9

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.

Keywords: dataset; residential; smart grid; smart meter; in-home display; IHD; HEMS; non-intrusive load monitoring; disaggregation; NILM (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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