The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Informing the planning of rotating power outages in heat waves through data analytics of connected smart thermostats for residential buildings
Abstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Death toll exceeded 70,000 in Europe during the summer of 2003
Robine, J.-M., Cheung, S. L. K., Le Roy, S., Van Oyen, H., Griffiths, C., Michel, J.-P., & Herrmann, F. R. (2007) · Comptes Rendus. Biologies · doi:10.1016/j.crvi.2007.12.001 · Cited by 2,127
Bouchama, A., & Knochel, J. P. (2002) · New England Journal of Medicine · doi:10.1056/nejmra011089 · Cited by 1,898
Research works printed but not linked: 20
- Robinson, 2001, On the definition of a heat wave, J. Appl. Meteorol. Climatol., 40, 762, 10.1175/1520-0450(2001)040<0762:OTDOAH>2.0.CO;2 · DOI
- Smoyer-Tomic, 2003, Heat wave hazards: an overview of heat wave impacts in Canada, Nat. Hazards, 28, 465, 10.1023/A:1022946528157 · DOI
- Meehl, 2004, More intense, more frequent, and longer lasting heat waves in the 21st century, Science, 305, 994, 10.1126/science.1098704 · DOI · Paper page
- Van Oldenborgh, 2018, Extreme heat in India and anthropogenic climate change, Nat. Hazard Earth Syst. Sci., 18, 365, 10.5194/nhess-18-365-2018 · DOI · Paper page
- Trenberth, 2012, Climate extremes and climate change: the Russian heat wave and other climate extremes of 2010, J. Geophys. Res. Atmos., 117, 10.1029/2012JD018020 · DOI
- Xia, 2016, The super-heat wave in eastern China during July–August 2013: a perspective of climate change, Int. J. Climatol., 36, 1291, 10.1002/joc.4424 · DOI
- Hoag, 2014, Russian summer tops ‘universal’ heatwave index, Nat. News, 10.1038/nature.2014.16250 · DOI
- Guo, 2018, Quantifying excess deaths related to heatwaves under climate change scenarios: a multi-country time series modelling study, PLoS Med., 15, 10.1371/journal.pmed.1002629 · DOI
- Sathaye, 2013, Estimating impacts of warming temperatures on California’s electricity system, Glob. Environ. Change, 23, 499, 10.1016/j.gloenvcha.2012.12.005 · DOI
- Zhao, 2021, Global multi-model projections of local urban climates, Nat. Clim. Change, 11, 152, 10.1038/s41558-020-00958-8 · DOI
- Ürge-Vorsatz, 2015, Heating and cooling energy trends and drivers in buildings, Renew. Sustain. Energy Rev., 41, 85, 10.1016/j.rser.2014.08.039 · DOI
- Fong, 2010, Comparative study of different solar cooling systems for buildings in subtropical city, Sol. Energy, 84, 227, 10.1016/j.solener.2009.11.002 · DOI
- Balaras, 2007, Solar air conditioning in Europe—an overview, Renew. Sustain. Energy Rev, 11, 299, 10.1016/j.rser.2005.02.003 · DOI · Paper page
- Pérez-Lombard, 2008, A review on buildings energy consumption information, Energy Build., 40, 394, 10.1016/j.enbuild.2007.03.007 · DOI · Paper page
- El-Dessouky, 2004, Performance analysis of two-stage evaporative coolers, Chem. Eng. J., 102, 255, 10.1016/j.cej.2004.01.036 · DOI
- Ruggles, 2020, Developing reliable hourly electricity demand data through screening and imputation, Sci. Data, 7, 1, 10.1038/s41597-020-0483-x · DOI
- Fraisse, 2000, Proposal for a highly intermittent heating law for discontinuously occupied buildings, Proc. Inst. Mech. Eng. Part J. Power Energy, 214, 29, 10.1243/0957650001537831 · DOI
- Wang, 2018, A model to compare convective and radiant heating systems for intermittent space heating, Appl. Energy, 215, 211, 10.1016/j.apenergy.2018.01.088 · DOI
- Wang, 2015, Modeling and measurement study on an intermittent heating system of a residence in Cambridgeshire, Build. Environ., 92, 380, 10.1016/j.buildenv.2015.05.014 · DOI
- Chen, 2016, Bridging the gap between sustainable technology adoption and protecting natural resources: predicting intentions to adopt energy management technologies in California, Energy Res. Soc. Sci., 22, 210, 10.1016/j.erss.2016.10.003 · DOI
Metadata
- Authors
- Wang, Z., Hong, T., & Li, H.
- Venue
- Environmental Research Letters
- Publisher
- IOP Publishing
- Published
- 2021-06-22
- Volume
- 16
- Issue
- 7
- Pages
- 074003
- ISSN
- 1748-9326
- Cited by
- 43
- References
- 2
- Licence
- Funders
- Building Technologies Program (DE-AC02-05CH11231)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review