A Novel Data-Driven Method With Decomposition Mechanism Suitable for Different Periods of Electrical Load Forecasting

For improving the precision of the load forecasting in different time spans, a new load forecasting model which combines the improved complete ensemble empirical mode decomposition algorithm based on adaptive noise (ICEEMDAN) algorithm, the least squares support vector machine (LS-SVM) and the long...
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Autor*in:	Xiang Wang [verfasserIn] Zhanxia Wu [verfasserIn] Junxiong Ge [verfasserIn] Zhanhao Zhang [verfasserIn] Pukun Lu [verfasserIn] Shunjiang Wang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Load forecasting improved complete ensemble empirical mode decomposition algorithm based on adaptive noise least squares support vector machine long short-term memory network

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 10(2022), Seite 56282-56295
Übergeordnetes Werk:	volume:10 ; year:2022 ; pages:56282-56295

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2022.3177604

Katalog-ID:	DOAJ043616186

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spelling	10.1109/ACCESS.2022.3177604 doi (DE-627)DOAJ043616186 (DE-599)DOAJf2f09f971d254fde9e2f7d1681cdf69a DE-627 ger DE-627 rakwb eng TK1-9971 Xiang Wang verfasserin aut A Novel Data-Driven Method With Decomposition Mechanism Suitable for Different Periods of Electrical Load Forecasting 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier For improving the precision of the load forecasting in different time spans, a new load forecasting model which combines the improved complete ensemble empirical mode decomposition algorithm based on adaptive noise (ICEEMDAN) algorithm, the least squares support vector machine (LS-SVM) and the long short-term memory network (LSTM) is proposed. In this paper, the training set of the forecasting model is acquired from the department of dispatch center from a large city in the north of China. And the advantages of the forecasting algorithms and decomposition algorithm are applied reasonably, where the ICEEMDAN algorithm is used to decompose the original historical load data. By using the ICEEMDAN algorithm, the fluctuation trend of different periods can be obtained. And the structure of the neural network combining LS-SVM and LSTM, which is used to obtain the load forecasting result. Based on LS-SVM and LSTM algorithm the non-stationary and stationary signals have been processed, respectively. The simulation results show that whether testing by the dataset acquired from a city in the north of China or the Elia dataset, the proposed method outperforms the load forecasting model in the short-, medium- and long-term which is based on PSO-SVR, LS-SVM, LSTM and ICCEMDAN-LSTM, respectively. Load forecasting improved complete ensemble empirical mode decomposition algorithm based on adaptive noise least squares support vector machine long short-term memory network Electrical engineering. Electronics. Nuclear engineering Zhanxia Wu verfasserin aut Junxiong Ge verfasserin aut Zhanhao Zhang verfasserin aut Pukun Lu verfasserin aut Shunjiang Wang verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 56282-56295 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:56282-56295 https://doi.org/10.1109/ACCESS.2022.3177604 kostenfrei https://doaj.org/article/f2f09f971d254fde9e2f7d1681cdf69a kostenfrei https://ieeexplore.ieee.org/document/9780398/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 56282-56295
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callnumber-first	T - Technology
author	Xiang Wang
spellingShingle	Xiang Wang misc TK1-9971 misc Load forecasting misc improved complete ensemble empirical mode decomposition algorithm based on adaptive noise misc least squares support vector machine misc long short-term memory network misc Electrical engineering. Electronics. Nuclear engineering A Novel Data-Driven Method With Decomposition Mechanism Suitable for Different Periods of Electrical Load Forecasting
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topic_title	TK1-9971 A Novel Data-Driven Method With Decomposition Mechanism Suitable for Different Periods of Electrical Load Forecasting Load forecasting improved complete ensemble empirical mode decomposition algorithm based on adaptive noise least squares support vector machine long short-term memory network
topic	misc TK1-9971 misc Load forecasting misc improved complete ensemble empirical mode decomposition algorithm based on adaptive noise misc least squares support vector machine misc long short-term memory network misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Load forecasting misc improved complete ensemble empirical mode decomposition algorithm based on adaptive noise misc least squares support vector machine misc long short-term memory network misc Electrical engineering. Electronics. Nuclear engineering
topic_browse	misc TK1-9971 misc Load forecasting misc improved complete ensemble empirical mode decomposition algorithm based on adaptive noise misc least squares support vector machine misc long short-term memory network misc Electrical engineering. Electronics. Nuclear engineering
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title	A Novel Data-Driven Method With Decomposition Mechanism Suitable for Different Periods of Electrical Load Forecasting
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title_full	A Novel Data-Driven Method With Decomposition Mechanism Suitable for Different Periods of Electrical Load Forecasting
author_sort	Xiang Wang
journal	IEEE Access
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author_browse	Xiang Wang Zhanxia Wu Junxiong Ge Zhanhao Zhang Pukun Lu Shunjiang Wang
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doi_str_mv	10.1109/ACCESS.2022.3177604
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title_sort	novel data-driven method with decomposition mechanism suitable for different periods of electrical load forecasting
callnumber	TK1-9971
title_auth	A Novel Data-Driven Method With Decomposition Mechanism Suitable for Different Periods of Electrical Load Forecasting
abstract	For improving the precision of the load forecasting in different time spans, a new load forecasting model which combines the improved complete ensemble empirical mode decomposition algorithm based on adaptive noise (ICEEMDAN) algorithm, the least squares support vector machine (LS-SVM) and the long short-term memory network (LSTM) is proposed. In this paper, the training set of the forecasting model is acquired from the department of dispatch center from a large city in the north of China. And the advantages of the forecasting algorithms and decomposition algorithm are applied reasonably, where the ICEEMDAN algorithm is used to decompose the original historical load data. By using the ICEEMDAN algorithm, the fluctuation trend of different periods can be obtained. And the structure of the neural network combining LS-SVM and LSTM, which is used to obtain the load forecasting result. Based on LS-SVM and LSTM algorithm the non-stationary and stationary signals have been processed, respectively. The simulation results show that whether testing by the dataset acquired from a city in the north of China or the Elia dataset, the proposed method outperforms the load forecasting model in the short-, medium- and long-term which is based on PSO-SVR, LS-SVM, LSTM and ICCEMDAN-LSTM, respectively.
abstractGer	For improving the precision of the load forecasting in different time spans, a new load forecasting model which combines the improved complete ensemble empirical mode decomposition algorithm based on adaptive noise (ICEEMDAN) algorithm, the least squares support vector machine (LS-SVM) and the long short-term memory network (LSTM) is proposed. In this paper, the training set of the forecasting model is acquired from the department of dispatch center from a large city in the north of China. And the advantages of the forecasting algorithms and decomposition algorithm are applied reasonably, where the ICEEMDAN algorithm is used to decompose the original historical load data. By using the ICEEMDAN algorithm, the fluctuation trend of different periods can be obtained. And the structure of the neural network combining LS-SVM and LSTM, which is used to obtain the load forecasting result. Based on LS-SVM and LSTM algorithm the non-stationary and stationary signals have been processed, respectively. The simulation results show that whether testing by the dataset acquired from a city in the north of China or the Elia dataset, the proposed method outperforms the load forecasting model in the short-, medium- and long-term which is based on PSO-SVR, LS-SVM, LSTM and ICCEMDAN-LSTM, respectively.
abstract_unstemmed	For improving the precision of the load forecasting in different time spans, a new load forecasting model which combines the improved complete ensemble empirical mode decomposition algorithm based on adaptive noise (ICEEMDAN) algorithm, the least squares support vector machine (LS-SVM) and the long short-term memory network (LSTM) is proposed. In this paper, the training set of the forecasting model is acquired from the department of dispatch center from a large city in the north of China. And the advantages of the forecasting algorithms and decomposition algorithm are applied reasonably, where the ICEEMDAN algorithm is used to decompose the original historical load data. By using the ICEEMDAN algorithm, the fluctuation trend of different periods can be obtained. And the structure of the neural network combining LS-SVM and LSTM, which is used to obtain the load forecasting result. Based on LS-SVM and LSTM algorithm the non-stationary and stationary signals have been processed, respectively. The simulation results show that whether testing by the dataset acquired from a city in the north of China or the Elia dataset, the proposed method outperforms the load forecasting model in the short-, medium- and long-term which is based on PSO-SVR, LS-SVM, LSTM and ICCEMDAN-LSTM, respectively.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
title_short	A Novel Data-Driven Method With Decomposition Mechanism Suitable for Different Periods of Electrical Load Forecasting
url	https://doi.org/10.1109/ACCESS.2022.3177604 https://doaj.org/article/f2f09f971d254fde9e2f7d1681cdf69a https://ieeexplore.ieee.org/document/9780398/ https://doaj.org/toc/2169-3536
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author2	Zhanxia Wu Junxiong Ge Zhanhao Zhang Pukun Lu Shunjiang Wang
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doi_str	10.1109/ACCESS.2022.3177604
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up_date	2024-07-03T18:31:53.057Z
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The simulation results show that whether testing by the dataset acquired from a city in the north of China or the Elia dataset, the proposed method outperforms the load forecasting model in the short-, medium- and long-term which is based on PSO-SVR, LS-SVM, LSTM and ICCEMDAN-LSTM, respectively.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Load forecasting</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">improved complete ensemble empirical mode decomposition algorithm based on adaptive noise</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">least squares support vector machine</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">long short-term memory network</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Electrical engineering. Electronics. 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code="d">IEEE, 2014</subfield><subfield code="g">10(2022), Seite 56282-56295</subfield><subfield code="w">(DE-627)728440385</subfield><subfield code="w">(DE-600)2687964-5</subfield><subfield code="x">21693536</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:10</subfield><subfield code="g">year:2022</subfield><subfield code="g">pages:56282-56295</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1109/ACCESS.2022.3177604</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/f2f09f971d254fde9e2f7d1681cdf69a</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://ieeexplore.ieee.org/document/9780398/</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield 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A Novel Data-Driven Method With Decomposition Mechanism Suitable for Different Periods of Electrical Load Forecasting

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