Load forecasting of refrigerated display cabinet based on CEEMD–IPSO–LSTM combined model

The load of the showcase is a nonlinear and unstable time series data, and the traditional forecasting method is not applicable. Deep learning algorithms are introduced to predict the load of the showcase. Based on the CEEMD–IPSO–LSTM combination algorithm, this paper builds a refrigerated display c...

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Autores principales:	Pei Yuan, Zhenglin Lei, Qinghui Zeng, Yixiao Wu, Yanli Lu, Chaolong Hu
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	food refrigerated display cabinet load forecasting improved particle swarm algorithm complementary ensemble empirical mode decomposition Physics QC1-999
Acceso en línea:	https://doaj.org/article/42baa8f438984cc5886a444fae2b3c8f
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id	oai:doaj.org-article:42baa8f438984cc5886a444fae2b3c8f
record_format	dspace
spelling	oai:doaj.org-article:42baa8f438984cc5886a444fae2b3c8f2021-12-05T14:11:02ZLoad forecasting of refrigerated display cabinet based on CEEMD–IPSO–LSTM combined model2391-547110.1515/phys-2021-0043https://doaj.org/article/42baa8f438984cc5886a444fae2b3c8f2021-07-01T00:00:00Zhttps://doi.org/10.1515/phys-2021-0043https://doaj.org/toc/2391-5471The load of the showcase is a nonlinear and unstable time series data, and the traditional forecasting method is not applicable. Deep learning algorithms are introduced to predict the load of the showcase. Based on the CEEMD–IPSO–LSTM combination algorithm, this paper builds a refrigerated display cabinet load forecasting model. Compared with the forecast results of other models, it finally proves that the CEEMD–IPSO–LSTM model has the highest load forecasting accuracy, and the model’s determination coefficient is 0.9105, which is obviously excellent. Compared with other models, the model constructed in this paper can predict the load of showcases, which can provide a reference for energy saving and consumption reduction of display cabinet.Pei YuanZhenglin LeiQinghui ZengYixiao WuYanli LuChaolong HuDe Gruyterarticlefood refrigerated display cabinetload forecastingimproved particle swarm algorithmcomplementary ensemble empirical mode decompositionPhysicsQC1-999ENOpen Physics, Vol 19, Iss 1, Pp 360-374 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	food refrigerated display cabinet load forecasting improved particle swarm algorithm complementary ensemble empirical mode decomposition Physics QC1-999
spellingShingle	food refrigerated display cabinet load forecasting improved particle swarm algorithm complementary ensemble empirical mode decomposition Physics QC1-999 Pei Yuan Zhenglin Lei Qinghui Zeng Yixiao Wu Yanli Lu Chaolong Hu Load forecasting of refrigerated display cabinet based on CEEMD–IPSO–LSTM combined model
description	The load of the showcase is a nonlinear and unstable time series data, and the traditional forecasting method is not applicable. Deep learning algorithms are introduced to predict the load of the showcase. Based on the CEEMD–IPSO–LSTM combination algorithm, this paper builds a refrigerated display cabinet load forecasting model. Compared with the forecast results of other models, it finally proves that the CEEMD–IPSO–LSTM model has the highest load forecasting accuracy, and the model’s determination coefficient is 0.9105, which is obviously excellent. Compared with other models, the model constructed in this paper can predict the load of showcases, which can provide a reference for energy saving and consumption reduction of display cabinet.
format	article
author	Pei Yuan Zhenglin Lei Qinghui Zeng Yixiao Wu Yanli Lu Chaolong Hu
author_facet	Pei Yuan Zhenglin Lei Qinghui Zeng Yixiao Wu Yanli Lu Chaolong Hu
author_sort	Pei Yuan
title	Load forecasting of refrigerated display cabinet based on CEEMD–IPSO–LSTM combined model
title_short	Load forecasting of refrigerated display cabinet based on CEEMD–IPSO–LSTM combined model
title_full	Load forecasting of refrigerated display cabinet based on CEEMD–IPSO–LSTM combined model
title_fullStr	Load forecasting of refrigerated display cabinet based on CEEMD–IPSO–LSTM combined model
title_full_unstemmed	Load forecasting of refrigerated display cabinet based on CEEMD–IPSO–LSTM combined model
title_sort	load forecasting of refrigerated display cabinet based on ceemd–ipso–lstm combined model
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/42baa8f438984cc5886a444fae2b3c8f
work_keys_str_mv	AT peiyuan loadforecastingofrefrigerateddisplaycabinetbasedonceemdipsolstmcombinedmodel AT zhenglinlei loadforecastingofrefrigerateddisplaycabinetbasedonceemdipsolstmcombinedmodel AT qinghuizeng loadforecastingofrefrigerateddisplaycabinetbasedonceemdipsolstmcombinedmodel AT yixiaowu loadforecastingofrefrigerateddisplaycabinetbasedonceemdipsolstmcombinedmodel AT yanlilu loadforecastingofrefrigerateddisplaycabinetbasedonceemdipsolstmcombinedmodel AT chaolonghu loadforecastingofrefrigerateddisplaycabinetbasedonceemdipsolstmcombinedmodel
_version_	1718371470758903808

Load forecasting of refrigerated display cabinet based on CEEMD–IPSO–LSTM combined model

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