Novel Cooperative Multi-Input Multilayer Perceptron Neural Network Performance Analysis with Application of Solar Irradiance Forecasting

To forecast solar irradiance with higher accuracy and generalization capability is challenging in the photovoltaic (PV) energy system. Meteorological parameters are highly influential in solar irradiance, leading to intermittent and randomicity. Forecasting using a single neural network model does n...

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Autores principales:	M. Madhiarasan, Mohamed Louzazni, Partha Pratim Roy
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Renewable energy sources TJ807-830
Acceso en línea:	https://doaj.org/article/28e741c325e346cdb00517e55d3303b0
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spelling	oai:doaj.org-article:28e741c325e346cdb00517e55d3303b02021-11-15T01:19:29ZNovel Cooperative Multi-Input Multilayer Perceptron Neural Network Performance Analysis with Application of Solar Irradiance Forecasting1687-529X10.1155/2021/7238293https://doaj.org/article/28e741c325e346cdb00517e55d3303b02021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/7238293https://doaj.org/toc/1687-529XTo forecast solar irradiance with higher accuracy and generalization capability is challenging in the photovoltaic (PV) energy system. Meteorological parameters are highly influential in solar irradiance, leading to intermittent and randomicity. Forecasting using a single neural network model does not have sufficient generalization ability to achieve the optimal forecasting of solar irradiance. This paper proposes a novel cooperative multi-input multilayer perceptron neural network (CMMLPNN) to mitigate the issues related to generalization and meteorological effects. Authors develop a proposed forecasting neural network model based on the amalgamation of two inputs, three inputs, four inputs, five inputs, and six inputs associated multilayer perceptron neural network. In the proposed forecasting model (CMMLPNN), the authors overcome the variance based on the meteorological parameters. The amalgamation of five multi-input multilayer perceptron neural networks leads to better generalization ability. Some individual multilayer perceptron neural network-based forecasting models outperform in some situations, but cannot assure generalization ability and suffer from the meteorological weather condition. The proposed CMMLPNN (cooperative multi-input multilayer perceptron neural network) achieves better forecasting accuracy with the generalization ability. Therefore, the proposed forecasting model is superior to other neural network-based forecasting models and existing models.M. MadhiarasanMohamed LouzazniPartha Pratim RoyHindawi LimitedarticleRenewable energy sourcesTJ807-830ENInternational Journal of Photoenergy, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Renewable energy sources TJ807-830
spellingShingle	Renewable energy sources TJ807-830 M. Madhiarasan Mohamed Louzazni Partha Pratim Roy Novel Cooperative Multi-Input Multilayer Perceptron Neural Network Performance Analysis with Application of Solar Irradiance Forecasting
description	To forecast solar irradiance with higher accuracy and generalization capability is challenging in the photovoltaic (PV) energy system. Meteorological parameters are highly influential in solar irradiance, leading to intermittent and randomicity. Forecasting using a single neural network model does not have sufficient generalization ability to achieve the optimal forecasting of solar irradiance. This paper proposes a novel cooperative multi-input multilayer perceptron neural network (CMMLPNN) to mitigate the issues related to generalization and meteorological effects. Authors develop a proposed forecasting neural network model based on the amalgamation of two inputs, three inputs, four inputs, five inputs, and six inputs associated multilayer perceptron neural network. In the proposed forecasting model (CMMLPNN), the authors overcome the variance based on the meteorological parameters. The amalgamation of five multi-input multilayer perceptron neural networks leads to better generalization ability. Some individual multilayer perceptron neural network-based forecasting models outperform in some situations, but cannot assure generalization ability and suffer from the meteorological weather condition. The proposed CMMLPNN (cooperative multi-input multilayer perceptron neural network) achieves better forecasting accuracy with the generalization ability. Therefore, the proposed forecasting model is superior to other neural network-based forecasting models and existing models.
format	article
author	M. Madhiarasan Mohamed Louzazni Partha Pratim Roy
author_facet	M. Madhiarasan Mohamed Louzazni Partha Pratim Roy
author_sort	M. Madhiarasan
title	Novel Cooperative Multi-Input Multilayer Perceptron Neural Network Performance Analysis with Application of Solar Irradiance Forecasting
title_short	Novel Cooperative Multi-Input Multilayer Perceptron Neural Network Performance Analysis with Application of Solar Irradiance Forecasting
title_full	Novel Cooperative Multi-Input Multilayer Perceptron Neural Network Performance Analysis with Application of Solar Irradiance Forecasting
title_fullStr	Novel Cooperative Multi-Input Multilayer Perceptron Neural Network Performance Analysis with Application of Solar Irradiance Forecasting
title_full_unstemmed	Novel Cooperative Multi-Input Multilayer Perceptron Neural Network Performance Analysis with Application of Solar Irradiance Forecasting
title_sort	novel cooperative multi-input multilayer perceptron neural network performance analysis with application of solar irradiance forecasting
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/28e741c325e346cdb00517e55d3303b0
work_keys_str_mv	AT mmadhiarasan novelcooperativemultiinputmultilayerperceptronneuralnetworkperformanceanalysiswithapplicationofsolarirradianceforecasting AT mohamedlouzazni novelcooperativemultiinputmultilayerperceptronneuralnetworkperformanceanalysiswithapplicationofsolarirradianceforecasting AT parthapratimroy novelcooperativemultiinputmultilayerperceptronneuralnetworkperformanceanalysiswithapplicationofsolarirradianceforecasting
_version_	1718428912088776704

Novel Cooperative Multi-Input Multilayer Perceptron Neural Network Performance Analysis with Application of Solar Irradiance Forecasting

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