TCNformer Model for Photovoltaic Power Prediction

Despite the growing capabilities of the short-term prediction of photovoltaic power, we still face two challenges to longer time-range predictions: error accumulation and long-term time series feature extraction. In order to improve the longer time range prediction accuracy of photovoltaic power, th...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Shipeng Liu [verfasserIn] Dejun Ning [verfasserIn] Jue Ma [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	transformer SkipGRU TCN photovoltaic power prediction time series data prediction

Übergeordnetes Werk:	In: Applied Sciences - MDPI AG, 2012, 13(2023), 4, p 2593
Übergeordnetes Werk:	volume:13 ; year:2023 ; number:4, p 2593

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/app13042593

Katalog-ID:	DOAJ081017812

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spelling	10.3390/app13042593 doi (DE-627)DOAJ081017812 (DE-599)DOAJ3228aa1a04ee471f8f92998a9300c59d DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Shipeng Liu verfasserin aut TCNformer Model for Photovoltaic Power Prediction 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Despite the growing capabilities of the short-term prediction of photovoltaic power, we still face two challenges to longer time-range predictions: error accumulation and long-term time series feature extraction. In order to improve the longer time range prediction accuracy of photovoltaic power, this paper proposes a seq2seq prediction model TCNformer, which outperforms other state-of-the-art (SOTA) algorithms by introducing variable selection (VS), long- and short-term time series feature extraction (LSTFE), and one-step temporal convolutional network (TCN) decoding. A VS module employs correlation analysis and periodic analysis to separate the time series correlation information, LSTFE extracts multiple time series features from time series data, and one-step TCN decoding realizes generative predictions. We demonstrate here that TCNformer has the lowest mean squared error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) in contrast to the other algorithms in the field of the short-term prediction of photovoltaic power, and furthermore, the effectiveness of each module has been verified through ablation experiments. transformer SkipGRU TCN photovoltaic power prediction time series data prediction Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Dejun Ning verfasserin aut Jue Ma verfasserin aut In Applied Sciences MDPI AG, 2012 13(2023), 4, p 2593 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:13 year:2023 number:4, p 2593 https://doi.org/10.3390/app13042593 kostenfrei https://doaj.org/article/3228aa1a04ee471f8f92998a9300c59d kostenfrei https://www.mdpi.com/2076-3417/13/4/2593 kostenfrei https://doaj.org/toc/2076-3417 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_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_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 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 13 2023 4, p 2593
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allfieldsSound	10.3390/app13042593 doi (DE-627)DOAJ081017812 (DE-599)DOAJ3228aa1a04ee471f8f92998a9300c59d DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Shipeng Liu verfasserin aut TCNformer Model for Photovoltaic Power Prediction 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Despite the growing capabilities of the short-term prediction of photovoltaic power, we still face two challenges to longer time-range predictions: error accumulation and long-term time series feature extraction. In order to improve the longer time range prediction accuracy of photovoltaic power, this paper proposes a seq2seq prediction model TCNformer, which outperforms other state-of-the-art (SOTA) algorithms by introducing variable selection (VS), long- and short-term time series feature extraction (LSTFE), and one-step temporal convolutional network (TCN) decoding. A VS module employs correlation analysis and periodic analysis to separate the time series correlation information, LSTFE extracts multiple time series features from time series data, and one-step TCN decoding realizes generative predictions. We demonstrate here that TCNformer has the lowest mean squared error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) in contrast to the other algorithms in the field of the short-term prediction of photovoltaic power, and furthermore, the effectiveness of each module has been verified through ablation experiments. transformer SkipGRU TCN photovoltaic power prediction time series data prediction Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Dejun Ning verfasserin aut Jue Ma verfasserin aut In Applied Sciences MDPI AG, 2012 13(2023), 4, p 2593 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:13 year:2023 number:4, p 2593 https://doi.org/10.3390/app13042593 kostenfrei https://doaj.org/article/3228aa1a04ee471f8f92998a9300c59d kostenfrei https://www.mdpi.com/2076-3417/13/4/2593 kostenfrei https://doaj.org/toc/2076-3417 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_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_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 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 13 2023 4, p 2593
language	English
source	In Applied Sciences 13(2023), 4, p 2593 volume:13 year:2023 number:4, p 2593
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topic_facet	transformer SkipGRU TCN photovoltaic power prediction time series data prediction Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry
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container_title	Applied Sciences
authorswithroles_txt_mv	Shipeng Liu @@aut@@ Dejun Ning @@aut@@ Jue Ma @@aut@@
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callnumber-first	T - Technology
author	Shipeng Liu
spellingShingle	Shipeng Liu misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc transformer misc SkipGRU misc TCN misc photovoltaic power prediction misc time series data prediction misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry TCNformer Model for Photovoltaic Power Prediction
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topic_title	TA1-2040 QH301-705.5 QC1-999 QD1-999 TCNformer Model for Photovoltaic Power Prediction transformer SkipGRU TCN photovoltaic power prediction time series data prediction
topic	misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc transformer misc SkipGRU misc TCN misc photovoltaic power prediction misc time series data prediction misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry
topic_unstemmed	misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc transformer misc SkipGRU misc TCN misc photovoltaic power prediction misc time series data prediction misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry
topic_browse	misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc transformer misc SkipGRU misc TCN misc photovoltaic power prediction misc time series data prediction misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry
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title	TCNformer Model for Photovoltaic Power Prediction
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title_full	TCNformer Model for Photovoltaic Power Prediction
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title_sort	tcnformer model for photovoltaic power prediction
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title_auth	TCNformer Model for Photovoltaic Power Prediction
abstract	Despite the growing capabilities of the short-term prediction of photovoltaic power, we still face two challenges to longer time-range predictions: error accumulation and long-term time series feature extraction. In order to improve the longer time range prediction accuracy of photovoltaic power, this paper proposes a seq2seq prediction model TCNformer, which outperforms other state-of-the-art (SOTA) algorithms by introducing variable selection (VS), long- and short-term time series feature extraction (LSTFE), and one-step temporal convolutional network (TCN) decoding. A VS module employs correlation analysis and periodic analysis to separate the time series correlation information, LSTFE extracts multiple time series features from time series data, and one-step TCN decoding realizes generative predictions. We demonstrate here that TCNformer has the lowest mean squared error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) in contrast to the other algorithms in the field of the short-term prediction of photovoltaic power, and furthermore, the effectiveness of each module has been verified through ablation experiments.
abstractGer	Despite the growing capabilities of the short-term prediction of photovoltaic power, we still face two challenges to longer time-range predictions: error accumulation and long-term time series feature extraction. In order to improve the longer time range prediction accuracy of photovoltaic power, this paper proposes a seq2seq prediction model TCNformer, which outperforms other state-of-the-art (SOTA) algorithms by introducing variable selection (VS), long- and short-term time series feature extraction (LSTFE), and one-step temporal convolutional network (TCN) decoding. A VS module employs correlation analysis and periodic analysis to separate the time series correlation information, LSTFE extracts multiple time series features from time series data, and one-step TCN decoding realizes generative predictions. We demonstrate here that TCNformer has the lowest mean squared error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) in contrast to the other algorithms in the field of the short-term prediction of photovoltaic power, and furthermore, the effectiveness of each module has been verified through ablation experiments.
abstract_unstemmed	Despite the growing capabilities of the short-term prediction of photovoltaic power, we still face two challenges to longer time-range predictions: error accumulation and long-term time series feature extraction. In order to improve the longer time range prediction accuracy of photovoltaic power, this paper proposes a seq2seq prediction model TCNformer, which outperforms other state-of-the-art (SOTA) algorithms by introducing variable selection (VS), long- and short-term time series feature extraction (LSTFE), and one-step temporal convolutional network (TCN) decoding. A VS module employs correlation analysis and periodic analysis to separate the time series correlation information, LSTFE extracts multiple time series features from time series data, and one-step TCN decoding realizes generative predictions. We demonstrate here that TCNformer has the lowest mean squared error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) in contrast to the other algorithms in the field of the short-term prediction of photovoltaic power, and furthermore, the effectiveness of each module has been verified through ablation experiments.
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title_short	TCNformer Model for Photovoltaic Power Prediction
url	https://doi.org/10.3390/app13042593 https://doaj.org/article/3228aa1a04ee471f8f92998a9300c59d https://www.mdpi.com/2076-3417/13/4/2593 https://doaj.org/toc/2076-3417
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