A decomposition‐based multi‐time dimension long short‐term memory model for short‐term electric load forecasting

Abstract Short‐term load forecasting is essential to power systems management. However, most existing forecasting methods fail to fully consider how to rationally integrate the intrinsic time‐related dimensions of electric load data and the decomposition methods into machine learning models so that...

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Autores principales:	Jiehui Huang, Zhiwang Zhou, Chunquan Li, Zhiyuan Liao, Peter X. Liu
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Distribution or transmission of electric power TK3001-3521 Production of electric energy or power. Powerplants. Central stations TK1001-1841
Acceso en línea:	https://doaj.org/article/b8ce4b4f36884e16bd712b294d9c7139
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spelling	oai:doaj.org-article:b8ce4b4f36884e16bd712b294d9c71392021-11-16T15:47:59ZA decomposition‐based multi‐time dimension long short‐term memory model for short‐term electric load forecasting1751-86951751-868710.1049/gtd2.12265https://doaj.org/article/b8ce4b4f36884e16bd712b294d9c71392021-12-01T00:00:00Zhttps://doi.org/10.1049/gtd2.12265https://doaj.org/toc/1751-8687https://doaj.org/toc/1751-8695Abstract Short‐term load forecasting is essential to power systems management. However, most existing forecasting methods fail to fully consider how to rationally integrate the intrinsic time‐related dimensions of electric load data and the decomposition methods into machine learning models so that their prediction accuracy and robustness still have much room for improvement. To solve this problem, this paper proposes a decomposition‐based multi‐time dimension long short‐term memory (DB‐MTD‐LSTM) model for short‐term electric load forecasting (STELF). In DB‐MTD‐LSTM, empirical mode decomposition with adaptive noise (CEEMDAN) is first introduced to smooth non‐linear non‐stationary electric load data and constrain the modal aliasing or noise of decomposed electric load data in the traditional decomposed method. A joint relevant time dimensions method (JRTDM) is then developed using autocorrelation analysis to rationally extract the temporal characteristics of decomposed data in multiple time dimensions. An improved LSTM called MTD‐LSTM is developed by combining JRTDM with LSTM, which can effectively apply multi‐dimensional time characteristics of the decomposed load to improve the predictive accuracy and robustness. Several datasets from Australia and China are performed to check the predictive performance of DB‐MTD‐LSTM. Experimental results verify that DB‐MTD‐LSTM has better predictive accuracy and satisfactory robustness compared with state‐of‐the‐art and conventional predictive models.Jiehui HuangZhiwang ZhouChunquan LiZhiyuan LiaoPeter X. LiuWileyarticleDistribution or transmission of electric powerTK3001-3521Production of electric energy or power. Powerplants. Central stationsTK1001-1841ENIET Generation, Transmission & Distribution, Vol 15, Iss 24, Pp 3459-3473 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Distribution or transmission of electric power TK3001-3521 Production of electric energy or power. Powerplants. Central stations TK1001-1841
spellingShingle	Distribution or transmission of electric power TK3001-3521 Production of electric energy or power. Powerplants. Central stations TK1001-1841 Jiehui Huang Zhiwang Zhou Chunquan Li Zhiyuan Liao Peter X. Liu A decomposition‐based multi‐time dimension long short‐term memory model for short‐term electric load forecasting
description	Abstract Short‐term load forecasting is essential to power systems management. However, most existing forecasting methods fail to fully consider how to rationally integrate the intrinsic time‐related dimensions of electric load data and the decomposition methods into machine learning models so that their prediction accuracy and robustness still have much room for improvement. To solve this problem, this paper proposes a decomposition‐based multi‐time dimension long short‐term memory (DB‐MTD‐LSTM) model for short‐term electric load forecasting (STELF). In DB‐MTD‐LSTM, empirical mode decomposition with adaptive noise (CEEMDAN) is first introduced to smooth non‐linear non‐stationary electric load data and constrain the modal aliasing or noise of decomposed electric load data in the traditional decomposed method. A joint relevant time dimensions method (JRTDM) is then developed using autocorrelation analysis to rationally extract the temporal characteristics of decomposed data in multiple time dimensions. An improved LSTM called MTD‐LSTM is developed by combining JRTDM with LSTM, which can effectively apply multi‐dimensional time characteristics of the decomposed load to improve the predictive accuracy and robustness. Several datasets from Australia and China are performed to check the predictive performance of DB‐MTD‐LSTM. Experimental results verify that DB‐MTD‐LSTM has better predictive accuracy and satisfactory robustness compared with state‐of‐the‐art and conventional predictive models.
format	article
author	Jiehui Huang Zhiwang Zhou Chunquan Li Zhiyuan Liao Peter X. Liu
author_facet	Jiehui Huang Zhiwang Zhou Chunquan Li Zhiyuan Liao Peter X. Liu
author_sort	Jiehui Huang
title	A decomposition‐based multi‐time dimension long short‐term memory model for short‐term electric load forecasting
title_short	A decomposition‐based multi‐time dimension long short‐term memory model for short‐term electric load forecasting
title_full	A decomposition‐based multi‐time dimension long short‐term memory model for short‐term electric load forecasting
title_fullStr	A decomposition‐based multi‐time dimension long short‐term memory model for short‐term electric load forecasting
title_full_unstemmed	A decomposition‐based multi‐time dimension long short‐term memory model for short‐term electric load forecasting
title_sort	decomposition‐based multi‐time dimension long short‐term memory model for short‐term electric load forecasting
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/b8ce4b4f36884e16bd712b294d9c7139
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A decomposition‐based multi‐time dimension long short‐term memory model for short‐term electric load forecasting

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