Prediction of solar direct irradiance in Iraq by using artificial neural network

Global solar irradiance is one of the main significant factors for designing and considering the volume of any solar station beside of it is usage in agricultural and building issue. Due of lack a precise information about the irradiance in Iraq metrological organization and seismology, this st...

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Autores principales:	zana Saleem, Gzing Adil Mohammed
Formato:	article
Lenguaje:	EN
Publicado:	Salahaddin University-Erbil 2021
Materias:	renewable energy solar system artificial neural network prediction. Technology T Science Q
Acceso en línea:	https://doaj.org/article/420a10300bb247ca875e65ba2ca71a0b
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spelling	oai:doaj.org-article:420a10300bb247ca875e65ba2ca71a0b2021-11-07T06:07:21ZPrediction of solar direct irradiance in Iraq by using artificial neural network10.21271/ZJPAS.33.5.52218-02302412-3986https://doaj.org/article/420a10300bb247ca875e65ba2ca71a0b2021-10-01T00:00:00Zhttps://zancojournals.su.edu.krd/index.php/JPAS/article/view/4058https://doaj.org/toc/2218-0230https://doaj.org/toc/2412-3986 Global solar irradiance is one of the main significant factors for designing and considering the volume of any solar station beside of it is usage in agricultural and building issue. Due of lack a precise information about the irradiance in Iraq metrological organization and seismology, this study is aimed to adopt the historical global data, build numerical analysis via using artificial neural network and predicting hourly irradiance. The test is applied over three locations Erbil, Bagdad, and Basra for being references to their closest locations. A foreword neural network (FNN) is the learning algorithm that is used in this study with relying on seven input variables consisting of Temperature, Precipitation, Humidity, Wind speed, Wind direction Sunshine duration and Date. After normalizing and standardizing data, an iteration method is used for determining the optimum number of neuron(s) in a hidden layer. It yields a least Root Mean square error (RMSE) between 2.5 to 3. The computed correlation coefficients are between 0.94 -0.96 for the mentioned locations.zana SaleemGzing Adil MohammedSalahaddin University-Erbilarticlerenewable energysolar systemartificial neural networkprediction.TechnologyTScienceQENZanco Journal of Pure and Applied Sciences, Vol 33, Iss 5, Pp 43-50 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	renewable energy solar system artificial neural network prediction. Technology T Science Q
spellingShingle	renewable energy solar system artificial neural network prediction. Technology T Science Q zana Saleem Gzing Adil Mohammed Prediction of solar direct irradiance in Iraq by using artificial neural network
description	Global solar irradiance is one of the main significant factors for designing and considering the volume of any solar station beside of it is usage in agricultural and building issue. Due of lack a precise information about the irradiance in Iraq metrological organization and seismology, this study is aimed to adopt the historical global data, build numerical analysis via using artificial neural network and predicting hourly irradiance. The test is applied over three locations Erbil, Bagdad, and Basra for being references to their closest locations. A foreword neural network (FNN) is the learning algorithm that is used in this study with relying on seven input variables consisting of Temperature, Precipitation, Humidity, Wind speed, Wind direction Sunshine duration and Date. After normalizing and standardizing data, an iteration method is used for determining the optimum number of neuron(s) in a hidden layer. It yields a least Root Mean square error (RMSE) between 2.5 to 3. The computed correlation coefficients are between 0.94 -0.96 for the mentioned locations.
format	article
author	zana Saleem Gzing Adil Mohammed
author_facet	zana Saleem Gzing Adil Mohammed
author_sort	zana Saleem
title	Prediction of solar direct irradiance in Iraq by using artificial neural network
title_short	Prediction of solar direct irradiance in Iraq by using artificial neural network
title_full	Prediction of solar direct irradiance in Iraq by using artificial neural network
title_fullStr	Prediction of solar direct irradiance in Iraq by using artificial neural network
title_full_unstemmed	Prediction of solar direct irradiance in Iraq by using artificial neural network
title_sort	prediction of solar direct irradiance in iraq by using artificial neural network
publisher	Salahaddin University-Erbil
publishDate	2021
url	https://doaj.org/article/420a10300bb247ca875e65ba2ca71a0b
work_keys_str_mv	AT zanasaleem predictionofsolardirectirradianceiniraqbyusingartificialneuralnetwork AT gzingadilmohammed predictionofsolardirectirradianceiniraqbyusingartificialneuralnetwork
_version_	1718443606182723584

Prediction of solar direct irradiance in Iraq by using artificial neural network

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