Predicting the energy output of hybrid PV–wind renewable energy system using feature selection technique for smart grids

In the current technological era, predicting the power and energy output based on the changing weather factors play an important role in the economic growth of the renewable energy sector. Unlike traditional fossil fuel-based resources, renewable energy sources potentially play a pivotal role in sus...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Zakria Qadir, Sara Imran Khan, Erfan Khalaji, Hafiz Suliman Munawar, Fadi Al-Turjman, M.A. Parvez Mahmud, Abbas Z. Kouzani, Khoa Le
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Smart grids Regression models Feature selection Prediction accuracy Renewable energy system ANN Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/d8721812563f4c18b53d563c2d556fd4
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:d8721812563f4c18b53d563c2d556fd4
record_format	dspace
spelling	oai:doaj.org-article:d8721812563f4c18b53d563c2d556fd42021-11-28T04:33:32ZPredicting the energy output of hybrid PV–wind renewable energy system using feature selection technique for smart grids2352-484710.1016/j.egyr.2021.01.018https://doaj.org/article/d8721812563f4c18b53d563c2d556fd42021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352484721000196https://doaj.org/toc/2352-4847In the current technological era, predicting the power and energy output based on the changing weather factors play an important role in the economic growth of the renewable energy sector. Unlike traditional fossil fuel-based resources, renewable energy sources potentially play a pivotal role in sustaining a country’s economy and improving the quality of life. As our planet is nowadays facing serious challenges due to climate change and global warming, this research could be effective to achieve good prediction accuracy in smart grids using different weather conditions. In the current study, different machine learning models are compared to estimate power and energy of hybrid photovoltaic (PV)-wind renewable energy systems using seven weather factors that have a significant impact on the output of the PV–wind renewable energy system. This study classified the machine learning model which could be potentially useful and efficient to predict energy and power. The historic hourly data is processed with and without data manipulation. While data manipulations are carried out using recursive feature elimination using cross-validation (RFECV). The data is trained using artificial neural network (ANN) regressors and correlations between different features within the dataset are identified. The main aim is to find meaningful patterns that could help statistical learning models train themselves based on these usage patterns. The results suggest that opting feature selection technique using linear regression model outperforms all the other models in all evaluation metrics having to mean squared error (MSE) of 0.000000104, mean absolute error (MAE) of 0.00083, R2 of 99.6%, and computation time of 0.02 s The results investigated depict that the sustainable computational scheme introduced has vast potential to enhance smart grids efficiency by predicting the energy produced by renewable energy systems.Zakria QadirSara Imran KhanErfan KhalajiHafiz Suliman MunawarFadi Al-TurjmanM.A. Parvez MahmudAbbas Z. KouzaniKhoa LeElsevierarticleSmart gridsRegression modelsFeature selectionPrediction accuracyRenewable energy systemANNElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENEnergy Reports, Vol 7, Iss , Pp 8465-8475 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Smart grids Regression models Feature selection Prediction accuracy Renewable energy system ANN Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Smart grids Regression models Feature selection Prediction accuracy Renewable energy system ANN Electrical engineering. Electronics. Nuclear engineering TK1-9971 Zakria Qadir Sara Imran Khan Erfan Khalaji Hafiz Suliman Munawar Fadi Al-Turjman M.A. Parvez Mahmud Abbas Z. Kouzani Khoa Le Predicting the energy output of hybrid PV–wind renewable energy system using feature selection technique for smart grids
description	In the current technological era, predicting the power and energy output based on the changing weather factors play an important role in the economic growth of the renewable energy sector. Unlike traditional fossil fuel-based resources, renewable energy sources potentially play a pivotal role in sustaining a country’s economy and improving the quality of life. As our planet is nowadays facing serious challenges due to climate change and global warming, this research could be effective to achieve good prediction accuracy in smart grids using different weather conditions. In the current study, different machine learning models are compared to estimate power and energy of hybrid photovoltaic (PV)-wind renewable energy systems using seven weather factors that have a significant impact on the output of the PV–wind renewable energy system. This study classified the machine learning model which could be potentially useful and efficient to predict energy and power. The historic hourly data is processed with and without data manipulation. While data manipulations are carried out using recursive feature elimination using cross-validation (RFECV). The data is trained using artificial neural network (ANN) regressors and correlations between different features within the dataset are identified. The main aim is to find meaningful patterns that could help statistical learning models train themselves based on these usage patterns. The results suggest that opting feature selection technique using linear regression model outperforms all the other models in all evaluation metrics having to mean squared error (MSE) of 0.000000104, mean absolute error (MAE) of 0.00083, R2 of 99.6%, and computation time of 0.02 s The results investigated depict that the sustainable computational scheme introduced has vast potential to enhance smart grids efficiency by predicting the energy produced by renewable energy systems.
format	article
author	Zakria Qadir Sara Imran Khan Erfan Khalaji Hafiz Suliman Munawar Fadi Al-Turjman M.A. Parvez Mahmud Abbas Z. Kouzani Khoa Le
author_facet	Zakria Qadir Sara Imran Khan Erfan Khalaji Hafiz Suliman Munawar Fadi Al-Turjman M.A. Parvez Mahmud Abbas Z. Kouzani Khoa Le
author_sort	Zakria Qadir
title	Predicting the energy output of hybrid PV–wind renewable energy system using feature selection technique for smart grids
title_short	Predicting the energy output of hybrid PV–wind renewable energy system using feature selection technique for smart grids
title_full	Predicting the energy output of hybrid PV–wind renewable energy system using feature selection technique for smart grids
title_fullStr	Predicting the energy output of hybrid PV–wind renewable energy system using feature selection technique for smart grids
title_full_unstemmed	Predicting the energy output of hybrid PV–wind renewable energy system using feature selection technique for smart grids
title_sort	predicting the energy output of hybrid pv–wind renewable energy system using feature selection technique for smart grids
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/d8721812563f4c18b53d563c2d556fd4
work_keys_str_mv	AT zakriaqadir predictingtheenergyoutputofhybridpvwindrenewableenergysystemusingfeatureselectiontechniqueforsmartgrids AT saraimrankhan predictingtheenergyoutputofhybridpvwindrenewableenergysystemusingfeatureselectiontechniqueforsmartgrids AT erfankhalaji predictingtheenergyoutputofhybridpvwindrenewableenergysystemusingfeatureselectiontechniqueforsmartgrids AT hafizsulimanmunawar predictingtheenergyoutputofhybridpvwindrenewableenergysystemusingfeatureselectiontechniqueforsmartgrids AT fadialturjman predictingtheenergyoutputofhybridpvwindrenewableenergysystemusingfeatureselectiontechniqueforsmartgrids AT maparvezmahmud predictingtheenergyoutputofhybridpvwindrenewableenergysystemusingfeatureselectiontechniqueforsmartgrids AT abbaszkouzani predictingtheenergyoutputofhybridpvwindrenewableenergysystemusingfeatureselectiontechniqueforsmartgrids AT khoale predictingtheenergyoutputofhybridpvwindrenewableenergysystemusingfeatureselectiontechniqueforsmartgrids
_version_	1718408348121956352

Predicting the energy output of hybrid PV–wind renewable energy system using feature selection technique for smart grids

Ejemplares similares