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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,...

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Detalles Bibliográficos
Autores principales: Liu, Guolong, Liu, Jinjie, Bai, Yan, Wang, Chengwei, Wang, Haosheng, Zhao, Huan, Liang, Gaoqi, Zhao, Junhua, Qiu, Jing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
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.
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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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