Informing the planning of rotating power outages in heat waves through data analytics of connected smart thermostats for residential buildings

Datos Bibliográficos

ID15544555
AutoresZhe Wang (0000-0002-3939-4452, Lawrence Berkeley National Laboratory), Tianzhen Hong (0000-0003-1886-9137, Lawrence Berkeley National Laboratory, autor de correspondencia), Han Li (0000-0002-4579-9552, Lawrence Berkeley National Laboratory)
Año2021
Volumen16
Número7
Páginas074003-074003
Fecha de publicación2021-06-08
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaEnvironmental Research Letters (JOURNAL)
Identificadores de la revistaISSN: 1748-9326 • E-ISSN: 1748-9326
EditorialIOP Publishing (PUBLISHER • GB)
DOI10.1088/1748-9326/ac092f
OpenAlexW3166907865
IdiomaEN
Citas recibidas2
Referencias citadas22

With climate change, heat waves have become more frequent and intense. Rotating power outages happen when the power supply is unable to meet the cooling demand increase resulting from extreme high temperatures. Power outages during heat waves expose residents to high risks of overheating. In this study, we propose a novel data-driven inverse modelling approach to inform decision makers and grid operators on planning rotating power outages. We first infer the building thermal characteristics using the connected smart thermostat data, and used the estimated thermal dynamics to simulate the thermal resilience during a heat wave event. Our proposed method was tested for the California power outage in August 2020 by using the open source Ecobee Donate Your Data dataset. We found in California the power outage should not last more than two hours during heat waves to avoid overheating risks. Informing the residents in advance so they can prepare for it through pre-cooling is a simple but effective strategy to expand the acceptable power outage duration. In addition to assisting power outage planning, the proposed method can be used for other applications, such as to evaluate a building energy efficiency policy, to examine fuel poverty, and to estimate the load shifting potential of building stocks

Climate change · Electrical engineering · Heat wave · Meteorology · Overheating (electricity · Reliability engineering · Thermostat · Building Energy and Comfort Optimization · Climate Change and Health Impacts · Computer Science · Engineering · Environmental Science · Wind and Air Flow Studies