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EWELD: A Large-Scale Industrial and Commercial Load Dataset in Extreme Weather Events
Load forecasting is crucial for the economic and secure operation of power systems. Extreme weather events, such as extreme heat and typhoons, can lead to more significant fluctuations in power consumption, making load forecasting more difficult. At present, due to the lack of relevant public data,...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10495315/ https://www.ncbi.nlm.nih.gov/pubmed/37696845 http://dx.doi.org/10.1038/s41597-023-02503-6 |
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author | Liu, Guolong Liu, Jinjie Bai, Yan Wang, Chengwei Wang, Haosheng Zhao, Huan Liang, Gaoqi Zhao, Junhua Qiu, Jing |
author_facet | Liu, Guolong Liu, Jinjie Bai, Yan Wang, Chengwei Wang, Haosheng Zhao, Huan Liang, Gaoqi Zhao, Junhua Qiu, Jing |
author_sort | Liu, Guolong |
collection | PubMed |
description | Load forecasting is crucial for the economic and secure operation of power systems. Extreme weather events, such as extreme heat and typhoons, can lead to more significant fluctuations in power consumption, making load forecasting more difficult. At present, due to the lack of relevant public data, the research on load forecasting under extreme weather events is still blank, so it is necessary to release a large-scale load dataset containing extreme weather events. The dataset includes electricity consumption data of industrial and commercial users under extreme weather events such as typhoons and extreme heat, which are collected at 15-minute intervals. The data is collected over six years from smart meters installed at the power entry points of users in southern China. The dataset consists of electricity consumption data from 386 industrial and commercial users in 17 industries, with more than 50 million records. During the recording period, extreme weather events such as typhoons and extreme heat are marked to form a total of 5,741 event records. |
format | Online Article Text |
id | pubmed-10495315 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-104953152023-09-13 EWELD: A Large-Scale Industrial and Commercial Load Dataset in Extreme Weather Events Liu, Guolong Liu, Jinjie Bai, Yan Wang, Chengwei Wang, Haosheng Zhao, Huan Liang, Gaoqi Zhao, Junhua Qiu, Jing Sci Data Data Descriptor Load forecasting is crucial for the economic and secure operation of power systems. Extreme weather events, such as extreme heat and typhoons, can lead to more significant fluctuations in power consumption, making load forecasting more difficult. At present, due to the lack of relevant public data, the research on load forecasting under extreme weather events is still blank, so it is necessary to release a large-scale load dataset containing extreme weather events. The dataset includes electricity consumption data of industrial and commercial users under extreme weather events such as typhoons and extreme heat, which are collected at 15-minute intervals. The data is collected over six years from smart meters installed at the power entry points of users in southern China. The dataset consists of electricity consumption data from 386 industrial and commercial users in 17 industries, with more than 50 million records. During the recording period, extreme weather events such as typhoons and extreme heat are marked to form a total of 5,741 event records. Nature Publishing Group UK 2023-09-11 /pmc/articles/PMC10495315/ /pubmed/37696845 http://dx.doi.org/10.1038/s41597-023-02503-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Liu, Guolong Liu, Jinjie Bai, Yan Wang, Chengwei Wang, Haosheng Zhao, Huan Liang, Gaoqi Zhao, Junhua Qiu, Jing EWELD: A Large-Scale Industrial and Commercial Load Dataset in Extreme Weather Events |
title | EWELD: A Large-Scale Industrial and Commercial Load Dataset in Extreme Weather Events |
title_full | EWELD: A Large-Scale Industrial and Commercial Load Dataset in Extreme Weather Events |
title_fullStr | EWELD: A Large-Scale Industrial and Commercial Load Dataset in Extreme Weather Events |
title_full_unstemmed | EWELD: A Large-Scale Industrial and Commercial Load Dataset in Extreme Weather Events |
title_short | EWELD: A Large-Scale Industrial and Commercial Load Dataset in Extreme Weather Events |
title_sort | eweld: a large-scale industrial and commercial load dataset in extreme weather events |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10495315/ https://www.ncbi.nlm.nih.gov/pubmed/37696845 http://dx.doi.org/10.1038/s41597-023-02503-6 |
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