Table 1 Selection of public load demand datasets.
From: A Large-Scale Residential Load Dataset in a Southern Province of China
Dataset | Country | Duration | Resolution | Description | Weather | Extreme event | Source |
|---|---|---|---|---|---|---|---|
SGSC | Australia | 2010–2014 | 30 min | 78,720 Houses | — | — | |
ADRES | 2009–2010 | 2 second | 30 Houses | — | — | ||
GREEND | Austria | 2013 | 1 second | 8 Houses | — | — | |
AMPds2 | Canada | 2012–2014 | 1 min | 1 Houses | Yes | — | |
EWELD | China | 2015–2022 | 15 min | 386 enterprises | Yes | Yes | |
— | 2016–2021 | 5 min | 5,600 buildings | Yes | — | ||
NOVAREF | German | 2013–2016 | 2 second | 12 Houses | — | — | |
DEDDIAG | 2016–2020 | 1 second | 15 Houses | — | — | ||
WPuQ | 2018–2020 | 10 second | 38 Houses | Yes | — | ||
CoSSMic | 2014–2019 | 1 min | 11 households | — | — | ||
ISSDA | Irish | 2009–2010 | 30 min | 5000 homes and businesses | — | — | |
— | Japan | 2012–2020 | 30 min | Over 7000 buildings | — | — | |
ENERTALK | Korean | 2016–2017 | 15 Hz | 22 Houses | — | — | |
FIKElectricity | Portugal | 2019 | 1 second | 3 Restaurant Kitchens | — | — | |
Elergone | 2011–2014 | 15 min | 370 clients | — | — | ||
— | Spain | 2014–2022 | 1 hour | 25,559 customers | — | — | |
LCL | UK | 2011–2014 | 30 min | 5567 Houses | — | — | |
NESEMP | 2010–2012 | 5 min | 215 Houses | Yes | — | ||
EDRP | 2007–2010 | 30 min | 14000 Houses | — | — | ||
IDEAL | 2018 | 1 second | 255 Houses | Yes | — | ||
METER | 2016–2019 | 1 min | 529 individuals | — | — | ||
REFIT | 2013–2015 | 8 second | 20 Houses | — | — | ||
SAVE | 2016–2019 | 15 min | Over 5000 homes | — | — | ||
SERL | 2021 | 30 min | 13000 Houses | Yes | — | ||
UK-DALE | 2013–2015 | 1 second | 5 Houses | — | — | ||
ECD-UY | Uruguay | 2019–2020 | 15 min | 110,953 customers | — | — | |
MFRED | USA | 2019 | 10 second | 390 apartments | — | — | |
EULP | 2018 | 15 min | 277 buildings | Yes | — | ||
Our dataset | China | 2022–2023 | 1 hour | 80,000 households | Yes | Yes | — |
