Evaluating neural network and linear regression photovoltaic power forecasting models based on different input methods

As Photovoltaic (PV) energy is impacted by various weather variables such as solar radiation and temperature, one of the key challenges facing solar energy forecasting is choosing the right inputs to achieve the most accurate prediction. Weather datasets, past power data sets, or both sets can be ut...

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Autores principales:	Mutaz AlShafeey, Csaba Csáki
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Solar energy Photovoltaic technology Prediction model Multiple regression Artificial neural network Prediction accuracy Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/3ae393c6d1f944e5874485ee9ad4f97d
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id	oai:doaj.org-article:3ae393c6d1f944e5874485ee9ad4f97d
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spelling	oai:doaj.org-article:3ae393c6d1f944e5874485ee9ad4f97d2021-11-20T05:07:09ZEvaluating neural network and linear regression photovoltaic power forecasting models based on different input methods2352-484710.1016/j.egyr.2021.10.125https://doaj.org/article/3ae393c6d1f944e5874485ee9ad4f97d2021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352484721011446https://doaj.org/toc/2352-4847As Photovoltaic (PV) energy is impacted by various weather variables such as solar radiation and temperature, one of the key challenges facing solar energy forecasting is choosing the right inputs to achieve the most accurate prediction. Weather datasets, past power data sets, or both sets can be utilized to build different forecasting models. However, operators of grid-connected PV farms do not always have full sets of data available to them especially over an extended period of time as required by key techniques such as multiple regression (MR) or artificial neural network (ANN). Therefore, the research reported here considered these two main approaches of building prediction models and compared their performance when utilizing structural, time-series, and hybrid methods for data input. Three years of PV power generation data (of an actual farm) as well as historical weather data (of the same location) with several key variables were collected and utilized to build and test six prediction models. Models were built and designed to forecast the PV power for a 24-hour ahead horizon with 15 min resolutions. Results of comparative performance analysis show that different models have different prediction accuracy depending on the input method used to build the model: ANN models perform better than the MR regardless of the input method used. The hybrid input method results in better prediction accuracy for both MR and ANN techniques, while using the time-series method results in the least accurate forecasting models. Furthermore, sensitivity analysis shows that poor data quality does impact forecasting accuracy negatively especially for the structural approach.Mutaz AlShafeeyCsaba CsákiElsevierarticleSolar energyPhotovoltaic technologyPrediction modelMultiple regressionArtificial neural networkPrediction accuracyElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENEnergy Reports, Vol 7, Iss , Pp 7601-7614 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Solar energy Photovoltaic technology Prediction model Multiple regression Artificial neural network Prediction accuracy Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Solar energy Photovoltaic technology Prediction model Multiple regression Artificial neural network Prediction accuracy Electrical engineering. Electronics. Nuclear engineering TK1-9971 Mutaz AlShafeey Csaba Csáki Evaluating neural network and linear regression photovoltaic power forecasting models based on different input methods
description	As Photovoltaic (PV) energy is impacted by various weather variables such as solar radiation and temperature, one of the key challenges facing solar energy forecasting is choosing the right inputs to achieve the most accurate prediction. Weather datasets, past power data sets, or both sets can be utilized to build different forecasting models. However, operators of grid-connected PV farms do not always have full sets of data available to them especially over an extended period of time as required by key techniques such as multiple regression (MR) or artificial neural network (ANN). Therefore, the research reported here considered these two main approaches of building prediction models and compared their performance when utilizing structural, time-series, and hybrid methods for data input. Three years of PV power generation data (of an actual farm) as well as historical weather data (of the same location) with several key variables were collected and utilized to build and test six prediction models. Models were built and designed to forecast the PV power for a 24-hour ahead horizon with 15 min resolutions. Results of comparative performance analysis show that different models have different prediction accuracy depending on the input method used to build the model: ANN models perform better than the MR regardless of the input method used. The hybrid input method results in better prediction accuracy for both MR and ANN techniques, while using the time-series method results in the least accurate forecasting models. Furthermore, sensitivity analysis shows that poor data quality does impact forecasting accuracy negatively especially for the structural approach.
format	article
author	Mutaz AlShafeey Csaba Csáki
author_facet	Mutaz AlShafeey Csaba Csáki
author_sort	Mutaz AlShafeey
title	Evaluating neural network and linear regression photovoltaic power forecasting models based on different input methods
title_short	Evaluating neural network and linear regression photovoltaic power forecasting models based on different input methods
title_full	Evaluating neural network and linear regression photovoltaic power forecasting models based on different input methods
title_fullStr	Evaluating neural network and linear regression photovoltaic power forecasting models based on different input methods
title_full_unstemmed	Evaluating neural network and linear regression photovoltaic power forecasting models based on different input methods
title_sort	evaluating neural network and linear regression photovoltaic power forecasting models based on different input methods
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/3ae393c6d1f944e5874485ee9ad4f97d
work_keys_str_mv	AT mutazalshafeey evaluatingneuralnetworkandlinearregressionphotovoltaicpowerforecastingmodelsbasedondifferentinputmethods AT csabacsaki evaluatingneuralnetworkandlinearregressionphotovoltaicpowerforecastingmodelsbasedondifferentinputmethods
_version_	1718419535933997056

Evaluating neural network and linear regression photovoltaic power forecasting models based on different input methods

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