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Using data from connected thermostats to track large power outages in the United States

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  • Meier, Alan
  • Ueno, Tsuyoshi
  • Pritoni, Marco

Abstract

The detection of power outages is an essential activity for electric utilities. A large, national dataset of Internet-connected thermostats was used to explore and illustrate the ability of Internet-connected devices to geospatially track outages caused by hurricanes and other major weather events. The method was applied to nine major outage events, including hurricanes and windstorms. In one event, Hurricane Irma, a network of about 1000 thermostats provided quantitatively similar results to detailed utility data with respect to the number of homes without power and identification of the most severely affected regions. The method generated regionally uniform outage data that would give emergency authorities additional visibility into the scope and magnitude of outages. The network of thermostat-sensors also made it possible to calculate a higher resolution version of outage duration (or SAIDI) at a level of customer-level visibility that was not previously available.

Suggested Citation

  • Meier, Alan & Ueno, Tsuyoshi & Pritoni, Marco, 2019. "Using data from connected thermostats to track large power outages in the United States," Applied Energy, Elsevier, vol. 256(C).
    • Handle: RePEc:eee:appene:v:256:y:2019:i:c:s0306261919316277
    DOI: 10.1016/j.apenergy.2019.113940
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    References listed on IDEAS

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    1. LaCommare, Kristina Hamachi & Eto, Joseph H. & Dunn, Laurel N. & Sohn, Michael D., 2018. "Improving the estimated cost of sustained power interruptions to electricity customers," Energy, Elsevier, vol. 153(C), pages 1038-1047.
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    Cited by:

    1. Vivian Do & Heather McBrien & Nina M. Flores & Alexander J. Northrop & Jeffrey Schlegelmilch & Mathew V. Kiang & Joan A. Casey, 2023. "Spatiotemporal distribution of power outages with climate events and social vulnerability in the USA," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    2. Sapountzoglou, Nikolaos & Lago, Jesus & De Schutter, Bart & Raison, Bertrand, 2020. "A generalizable and sensor-independent deep learning method for fault detection and location in low-voltage distribution grids," Applied Energy, Elsevier, vol. 276(C).
    3. Omar Mata & Juana Isabel Méndez & Pedro Ponce & Therese Peffer & Alan Meier & Arturo Molina, 2023. "Energy Savings in Buildings Based on Image Depth Sensors for Human Activity Recognition," Energies, MDPI, vol. 16(3), pages 1-22, January.
    4. Lee, Zachary E. & Max Zhang, K., 2022. "Unintended consequences of smart thermostats in the transition to electrified heating," Applied Energy, Elsevier, vol. 322(C).

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