An Extensible Framework for Short-Term Holiday Load Forecasting Combining Dynamic Time Warping and LSTM Network

Due to the extreme change of human behavior, the load consumption in public holidays fluctuates more significantly compared to general weekdays resulting in the difficulty of hourly holiday load forecasting. The holiday load forecasting is even challenging because the forecast is practically predict...
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Autor*in:	Jeffrey Gunawan [verfasserIn] Chin-Ya Huang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Holiday load forecasting short-term load forecasting (STLF) long-short term memory (LSTM) dynamic time warping (DTW)

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 9(2021), Seite 106885-106894
Übergeordnetes Werk:	volume:9 ; year:2021 ; pages:106885-106894

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2021.3099981

Katalog-ID:	DOAJ005822920

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520			\|a Due to the extreme change of human behavior, the load consumption in public holidays fluctuates more significantly compared to general weekdays resulting in the difficulty of hourly holiday load forecasting. The holiday load forecasting is even challenging because the forecast is practically predicted on the nearest workday which might be more than one days prior to the public holiday. In this paper, we propose a Joint Dynamic time warping and LSTM (JDL) framework, to predict the hourly holiday load consumption on the nearest workday which is at least one day before the incoming holiday. The proposed JDL is a hybrid short-term holiday forecasting framework which combines dynamic time warping (DTW) and long-short term memory (LSTM) network. The DTW predicts the load consumption of the nearest workday and any preceding compensatory holiday(s), if any, based on the similar holiday occurrence pattern. LSTM predicts the highly unpredictable load consumption of the target holiday by univariate and multivariate models. Current results show the proposed JDL outperforms others.
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spelling	10.1109/ACCESS.2021.3099981 doi (DE-627)DOAJ005822920 (DE-599)DOAJ56ed59b3ec994186b4a82571863cd14d DE-627 ger DE-627 rakwb eng TK1-9971 Jeffrey Gunawan verfasserin aut An Extensible Framework for Short-Term Holiday Load Forecasting Combining Dynamic Time Warping and LSTM Network 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Due to the extreme change of human behavior, the load consumption in public holidays fluctuates more significantly compared to general weekdays resulting in the difficulty of hourly holiday load forecasting. The holiday load forecasting is even challenging because the forecast is practically predicted on the nearest workday which might be more than one days prior to the public holiday. In this paper, we propose a Joint Dynamic time warping and LSTM (JDL) framework, to predict the hourly holiday load consumption on the nearest workday which is at least one day before the incoming holiday. The proposed JDL is a hybrid short-term holiday forecasting framework which combines dynamic time warping (DTW) and long-short term memory (LSTM) network. The DTW predicts the load consumption of the nearest workday and any preceding compensatory holiday(s), if any, based on the similar holiday occurrence pattern. LSTM predicts the highly unpredictable load consumption of the target holiday by univariate and multivariate models. Current results show the proposed JDL outperforms others. Holiday load forecasting short-term load forecasting (STLF) long-short term memory (LSTM) dynamic time warping (DTW) Electrical engineering. Electronics. Nuclear engineering Chin-Ya Huang verfasserin aut In IEEE Access IEEE, 2014 9(2021), Seite 106885-106894 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:9 year:2021 pages:106885-106894 https://doi.org/10.1109/ACCESS.2021.3099981 kostenfrei https://doaj.org/article/56ed59b3ec994186b4a82571863cd14d kostenfrei https://ieeexplore.ieee.org/document/9495765/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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 9 2021 106885-106894
allfields_unstemmed	10.1109/ACCESS.2021.3099981 doi (DE-627)DOAJ005822920 (DE-599)DOAJ56ed59b3ec994186b4a82571863cd14d DE-627 ger DE-627 rakwb eng TK1-9971 Jeffrey Gunawan verfasserin aut An Extensible Framework for Short-Term Holiday Load Forecasting Combining Dynamic Time Warping and LSTM Network 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Due to the extreme change of human behavior, the load consumption in public holidays fluctuates more significantly compared to general weekdays resulting in the difficulty of hourly holiday load forecasting. The holiday load forecasting is even challenging because the forecast is practically predicted on the nearest workday which might be more than one days prior to the public holiday. In this paper, we propose a Joint Dynamic time warping and LSTM (JDL) framework, to predict the hourly holiday load consumption on the nearest workday which is at least one day before the incoming holiday. The proposed JDL is a hybrid short-term holiday forecasting framework which combines dynamic time warping (DTW) and long-short term memory (LSTM) network. The DTW predicts the load consumption of the nearest workday and any preceding compensatory holiday(s), if any, based on the similar holiday occurrence pattern. LSTM predicts the highly unpredictable load consumption of the target holiday by univariate and multivariate models. Current results show the proposed JDL outperforms others. Holiday load forecasting short-term load forecasting (STLF) long-short term memory (LSTM) dynamic time warping (DTW) Electrical engineering. Electronics. Nuclear engineering Chin-Ya Huang verfasserin aut In IEEE Access IEEE, 2014 9(2021), Seite 106885-106894 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:9 year:2021 pages:106885-106894 https://doi.org/10.1109/ACCESS.2021.3099981 kostenfrei https://doaj.org/article/56ed59b3ec994186b4a82571863cd14d kostenfrei https://ieeexplore.ieee.org/document/9495765/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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 9 2021 106885-106894
allfieldsGer	10.1109/ACCESS.2021.3099981 doi (DE-627)DOAJ005822920 (DE-599)DOAJ56ed59b3ec994186b4a82571863cd14d DE-627 ger DE-627 rakwb eng TK1-9971 Jeffrey Gunawan verfasserin aut An Extensible Framework for Short-Term Holiday Load Forecasting Combining Dynamic Time Warping and LSTM Network 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Due to the extreme change of human behavior, the load consumption in public holidays fluctuates more significantly compared to general weekdays resulting in the difficulty of hourly holiday load forecasting. The holiday load forecasting is even challenging because the forecast is practically predicted on the nearest workday which might be more than one days prior to the public holiday. In this paper, we propose a Joint Dynamic time warping and LSTM (JDL) framework, to predict the hourly holiday load consumption on the nearest workday which is at least one day before the incoming holiday. The proposed JDL is a hybrid short-term holiday forecasting framework which combines dynamic time warping (DTW) and long-short term memory (LSTM) network. The DTW predicts the load consumption of the nearest workday and any preceding compensatory holiday(s), if any, based on the similar holiday occurrence pattern. LSTM predicts the highly unpredictable load consumption of the target holiday by univariate and multivariate models. Current results show the proposed JDL outperforms others. Holiday load forecasting short-term load forecasting (STLF) long-short term memory (LSTM) dynamic time warping (DTW) Electrical engineering. Electronics. Nuclear engineering Chin-Ya Huang verfasserin aut In IEEE Access IEEE, 2014 9(2021), Seite 106885-106894 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:9 year:2021 pages:106885-106894 https://doi.org/10.1109/ACCESS.2021.3099981 kostenfrei https://doaj.org/article/56ed59b3ec994186b4a82571863cd14d kostenfrei https://ieeexplore.ieee.org/document/9495765/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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 9 2021 106885-106894
allfieldsSound	10.1109/ACCESS.2021.3099981 doi (DE-627)DOAJ005822920 (DE-599)DOAJ56ed59b3ec994186b4a82571863cd14d DE-627 ger DE-627 rakwb eng TK1-9971 Jeffrey Gunawan verfasserin aut An Extensible Framework for Short-Term Holiday Load Forecasting Combining Dynamic Time Warping and LSTM Network 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Due to the extreme change of human behavior, the load consumption in public holidays fluctuates more significantly compared to general weekdays resulting in the difficulty of hourly holiday load forecasting. The holiday load forecasting is even challenging because the forecast is practically predicted on the nearest workday which might be more than one days prior to the public holiday. In this paper, we propose a Joint Dynamic time warping and LSTM (JDL) framework, to predict the hourly holiday load consumption on the nearest workday which is at least one day before the incoming holiday. The proposed JDL is a hybrid short-term holiday forecasting framework which combines dynamic time warping (DTW) and long-short term memory (LSTM) network. The DTW predicts the load consumption of the nearest workday and any preceding compensatory holiday(s), if any, based on the similar holiday occurrence pattern. LSTM predicts the highly unpredictable load consumption of the target holiday by univariate and multivariate models. Current results show the proposed JDL outperforms others. Holiday load forecasting short-term load forecasting (STLF) long-short term memory (LSTM) dynamic time warping (DTW) Electrical engineering. Electronics. Nuclear engineering Chin-Ya Huang verfasserin aut In IEEE Access IEEE, 2014 9(2021), Seite 106885-106894 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:9 year:2021 pages:106885-106894 https://doi.org/10.1109/ACCESS.2021.3099981 kostenfrei https://doaj.org/article/56ed59b3ec994186b4a82571863cd14d kostenfrei https://ieeexplore.ieee.org/document/9495765/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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 9 2021 106885-106894
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source	In IEEE Access 9(2021), Seite 106885-106894 volume:9 year:2021 pages:106885-106894
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topic_facet	Holiday load forecasting short-term load forecasting (STLF) long-short term memory (LSTM) dynamic time warping (DTW) Electrical engineering. Electronics. Nuclear engineering
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callnumber-first	T - Technology
author	Jeffrey Gunawan
spellingShingle	Jeffrey Gunawan misc TK1-9971 misc Holiday load forecasting misc short-term load forecasting (STLF) misc long-short term memory (LSTM) misc dynamic time warping (DTW) misc Electrical engineering. Electronics. Nuclear engineering An Extensible Framework for Short-Term Holiday Load Forecasting Combining Dynamic Time Warping and LSTM Network
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topic_title	TK1-9971 An Extensible Framework for Short-Term Holiday Load Forecasting Combining Dynamic Time Warping and LSTM Network Holiday load forecasting short-term load forecasting (STLF) long-short term memory (LSTM) dynamic time warping (DTW)
topic	misc TK1-9971 misc Holiday load forecasting misc short-term load forecasting (STLF) misc long-short term memory (LSTM) misc dynamic time warping (DTW) misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Holiday load forecasting misc short-term load forecasting (STLF) misc long-short term memory (LSTM) misc dynamic time warping (DTW) misc Electrical engineering. Electronics. Nuclear engineering
topic_browse	misc TK1-9971 misc Holiday load forecasting misc short-term load forecasting (STLF) misc long-short term memory (LSTM) misc dynamic time warping (DTW) misc Electrical engineering. Electronics. Nuclear engineering
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title	An Extensible Framework for Short-Term Holiday Load Forecasting Combining Dynamic Time Warping and LSTM Network
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title_sort	extensible framework for short-term holiday load forecasting combining dynamic time warping and lstm network
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title_auth	An Extensible Framework for Short-Term Holiday Load Forecasting Combining Dynamic Time Warping and LSTM Network
abstract	Due to the extreme change of human behavior, the load consumption in public holidays fluctuates more significantly compared to general weekdays resulting in the difficulty of hourly holiday load forecasting. The holiday load forecasting is even challenging because the forecast is practically predicted on the nearest workday which might be more than one days prior to the public holiday. In this paper, we propose a Joint Dynamic time warping and LSTM (JDL) framework, to predict the hourly holiday load consumption on the nearest workday which is at least one day before the incoming holiday. The proposed JDL is a hybrid short-term holiday forecasting framework which combines dynamic time warping (DTW) and long-short term memory (LSTM) network. The DTW predicts the load consumption of the nearest workday and any preceding compensatory holiday(s), if any, based on the similar holiday occurrence pattern. LSTM predicts the highly unpredictable load consumption of the target holiday by univariate and multivariate models. Current results show the proposed JDL outperforms others.
abstractGer	Due to the extreme change of human behavior, the load consumption in public holidays fluctuates more significantly compared to general weekdays resulting in the difficulty of hourly holiday load forecasting. The holiday load forecasting is even challenging because the forecast is practically predicted on the nearest workday which might be more than one days prior to the public holiday. In this paper, we propose a Joint Dynamic time warping and LSTM (JDL) framework, to predict the hourly holiday load consumption on the nearest workday which is at least one day before the incoming holiday. The proposed JDL is a hybrid short-term holiday forecasting framework which combines dynamic time warping (DTW) and long-short term memory (LSTM) network. The DTW predicts the load consumption of the nearest workday and any preceding compensatory holiday(s), if any, based on the similar holiday occurrence pattern. LSTM predicts the highly unpredictable load consumption of the target holiday by univariate and multivariate models. Current results show the proposed JDL outperforms others.
abstract_unstemmed	Due to the extreme change of human behavior, the load consumption in public holidays fluctuates more significantly compared to general weekdays resulting in the difficulty of hourly holiday load forecasting. The holiday load forecasting is even challenging because the forecast is practically predicted on the nearest workday which might be more than one days prior to the public holiday. In this paper, we propose a Joint Dynamic time warping and LSTM (JDL) framework, to predict the hourly holiday load consumption on the nearest workday which is at least one day before the incoming holiday. The proposed JDL is a hybrid short-term holiday forecasting framework which combines dynamic time warping (DTW) and long-short term memory (LSTM) network. The DTW predicts the load consumption of the nearest workday and any preceding compensatory holiday(s), if any, based on the similar holiday occurrence pattern. LSTM predicts the highly unpredictable load consumption of the target holiday by univariate and multivariate models. Current results show the proposed JDL outperforms others.
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title_short	An Extensible Framework for Short-Term Holiday Load Forecasting Combining Dynamic Time Warping and LSTM Network
url	https://doi.org/10.1109/ACCESS.2021.3099981 https://doaj.org/article/56ed59b3ec994186b4a82571863cd14d https://ieeexplore.ieee.org/document/9495765/ https://doaj.org/toc/2169-3536
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An Extensible Framework for Short-Term Holiday Load Forecasting Combining Dynamic Time Warping and LSTM Network

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