Enhanced Deep Belief Network Based on Ensemble Learning and Tree-Structured of Parzen Estimators: An Optimal Photovoltaic Power Forecasting Method

The random fluctuation and non-uniformity of Photovoltaic (PV) power generation greatly affect the power grids’ stability and operation. This paper addresses the high volatility of PV power by proposing a precise and reliable ensemble learning model for short-term PV power generation fore...

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Autores principales:	Mohamed Massaoudi, Haitham Abu-Rub, Shady S. Refaat, Mohamed Trabelsi, Ines Chihi, Fakhreddine S. Oueslati
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Deep belief network PV power forecasting stacking ensemble smart grid power generation planning Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/5b9c140af32240588b39dec97d3d223c
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spelling	oai:doaj.org-article:5b9c140af32240588b39dec97d3d223c2021-11-18T00:08:21ZEnhanced Deep Belief Network Based on Ensemble Learning and Tree-Structured of Parzen Estimators: An Optimal Photovoltaic Power Forecasting Method2169-353610.1109/ACCESS.2021.3125895https://doaj.org/article/5b9c140af32240588b39dec97d3d223c2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9605688/https://doaj.org/toc/2169-3536The random fluctuation and non-uniformity of Photovoltaic (PV) power generation greatly affect the power grids’ stability and operation. This paper addresses the high volatility of PV power by proposing a precise and reliable ensemble learning model for short-term PV power generation forecasting. The proposed forecasting tool incorporates a base model and meta-model layers. The first-layer base learner combines extreme learning machines, extremely randomized trees, k-nearest neighbor, and mondrian forest models. The meta-model layer exploits deep belief network to generate the final outputs. The hyper-parameters of the proposed stacking ensemble are carefully tuned using the tree-structured of parzen estimators algorithm to achieve top-notch predictive performance. The proposed model is thoroughly assessed through an empirical study using a real data set from Australia. The simulation results confirm the performance superiority of the proposed model over the existing forecasting models with the lowest average root mean square error and mean absolute percentage error of 3.88kW and 2.30%, respectively.Mohamed MassaoudiHaitham Abu-RubShady S. RefaatMohamed TrabelsiInes ChihiFakhreddine S. OueslatiIEEEarticleDeep belief networkPV power forecastingstacking ensemblesmart gridpower generation planningElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 150330-150344 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Deep belief network PV power forecasting stacking ensemble smart grid power generation planning Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Deep belief network PV power forecasting stacking ensemble smart grid power generation planning Electrical engineering. Electronics. Nuclear engineering TK1-9971 Mohamed Massaoudi Haitham Abu-Rub Shady S. Refaat Mohamed Trabelsi Ines Chihi Fakhreddine S. Oueslati Enhanced Deep Belief Network Based on Ensemble Learning and Tree-Structured of Parzen Estimators: An Optimal Photovoltaic Power Forecasting Method
description	The random fluctuation and non-uniformity of Photovoltaic (PV) power generation greatly affect the power grids’ stability and operation. This paper addresses the high volatility of PV power by proposing a precise and reliable ensemble learning model for short-term PV power generation forecasting. The proposed forecasting tool incorporates a base model and meta-model layers. The first-layer base learner combines extreme learning machines, extremely randomized trees, k-nearest neighbor, and mondrian forest models. The meta-model layer exploits deep belief network to generate the final outputs. The hyper-parameters of the proposed stacking ensemble are carefully tuned using the tree-structured of parzen estimators algorithm to achieve top-notch predictive performance. The proposed model is thoroughly assessed through an empirical study using a real data set from Australia. The simulation results confirm the performance superiority of the proposed model over the existing forecasting models with the lowest average root mean square error and mean absolute percentage error of 3.88kW and 2.30%, respectively.
format	article
author	Mohamed Massaoudi Haitham Abu-Rub Shady S. Refaat Mohamed Trabelsi Ines Chihi Fakhreddine S. Oueslati
author_facet	Mohamed Massaoudi Haitham Abu-Rub Shady S. Refaat Mohamed Trabelsi Ines Chihi Fakhreddine S. Oueslati
author_sort	Mohamed Massaoudi
title	Enhanced Deep Belief Network Based on Ensemble Learning and Tree-Structured of Parzen Estimators: An Optimal Photovoltaic Power Forecasting Method
title_short	Enhanced Deep Belief Network Based on Ensemble Learning and Tree-Structured of Parzen Estimators: An Optimal Photovoltaic Power Forecasting Method
title_full	Enhanced Deep Belief Network Based on Ensemble Learning and Tree-Structured of Parzen Estimators: An Optimal Photovoltaic Power Forecasting Method
title_fullStr	Enhanced Deep Belief Network Based on Ensemble Learning and Tree-Structured of Parzen Estimators: An Optimal Photovoltaic Power Forecasting Method
title_full_unstemmed	Enhanced Deep Belief Network Based on Ensemble Learning and Tree-Structured of Parzen Estimators: An Optimal Photovoltaic Power Forecasting Method
title_sort	enhanced deep belief network based on ensemble learning and tree-structured of parzen estimators: an optimal photovoltaic power forecasting method
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/5b9c140af32240588b39dec97d3d223c
work_keys_str_mv	AT mohamedmassaoudi enhanceddeepbeliefnetworkbasedonensemblelearningandtreestructuredofparzenestimatorsanoptimalphotovoltaicpowerforecastingmethod AT haithamaburub enhanceddeepbeliefnetworkbasedonensemblelearningandtreestructuredofparzenestimatorsanoptimalphotovoltaicpowerforecastingmethod AT shadysrefaat enhanceddeepbeliefnetworkbasedonensemblelearningandtreestructuredofparzenestimatorsanoptimalphotovoltaicpowerforecastingmethod AT mohamedtrabelsi enhanceddeepbeliefnetworkbasedonensemblelearningandtreestructuredofparzenestimatorsanoptimalphotovoltaicpowerforecastingmethod AT ineschihi enhanceddeepbeliefnetworkbasedonensemblelearningandtreestructuredofparzenestimatorsanoptimalphotovoltaicpowerforecastingmethod AT fakhreddinesoueslati enhanceddeepbeliefnetworkbasedonensemblelearningandtreestructuredofparzenestimatorsanoptimalphotovoltaicpowerforecastingmethod
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Enhanced Deep Belief Network Based on Ensemble Learning and Tree-Structured of Parzen Estimators: An Optimal Photovoltaic Power Forecasting Method

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