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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Stephen Makonin & Z. Jane Wang & Chris Tumpach, 2018. "RAE: The Rainforest Automation Energy Dataset for Smart Grid Meter Data Analysis," Data, MDPI, vol. 3(1), pages 1-9, February.
    • Handle: RePEc:gam:jdataj:v:3:y:2018:i:1:p:8-:d:131517
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    Citations

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    Cited by:

    1. Lucas Pereira, 2019. "NILMPEds: A Performance Evaluation Dataset for Event Detection Algorithms in Non-Intrusive Load Monitoring," Data, MDPI, vol. 4(3), pages 1-9, August.
    2. Altaf Hussain & Muhammad Aleem, 2018. "GoCJ: Google Cloud Jobs Dataset for Distributed and Cloud Computing Infrastructures," Data, MDPI, vol. 3(4), pages 1-12, September.

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