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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2021-01-01
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| Series: | International Journal of Photoenergy |
| Online Access: | http://dx.doi.org/10.1155/2021/7238293 |
| _version_ | 1798033219778510848 |
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| author | M. Madhiarasan Mohamed Louzazni Partha Pratim Roy |
| author_facet | M. Madhiarasan Mohamed Louzazni Partha Pratim Roy |
| author_sort | M. Madhiarasan |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-11T20:26:51Z |
| format | Article |
| id | doaj.art-28e741c325e346cdb00517e55d3303b0 |
| institution | Directory Open Access Journal |
| issn | 1687-529X |
| language | English |
| last_indexed | 2024-04-11T20:26:51Z |
| publishDate | 2021-01-01 |
| publisher | Hindawi Limited |
| record_format | Article |
| series | International Journal of Photoenergy |
| spelling | doaj.art-28e741c325e346cdb00517e55d3303b02022-12-22T04:04:38ZengHindawi LimitedInternational Journal of Photoenergy1687-529X2021-01-01202110.1155/2021/7238293Novel Cooperative Multi-Input Multilayer Perceptron Neural Network Performance Analysis with Application of Solar Irradiance ForecastingM. Madhiarasan0Mohamed Louzazni1Partha Pratim Roy2Department of Computer Science and EngineeringScience Engineer Laboratory for EnergyDepartment of Computer Science and EngineeringTo 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.http://dx.doi.org/10.1155/2021/7238293 |
| spellingShingle | M. Madhiarasan Mohamed Louzazni Partha Pratim Roy Novel Cooperative Multi-Input Multilayer Perceptron Neural Network Performance Analysis with Application of Solar Irradiance Forecasting International Journal of Photoenergy |
| title | 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_short | 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 |
| url | http://dx.doi.org/10.1155/2021/7238293 |
| work_keys_str_mv | AT mmadhiarasan novelcooperativemultiinputmultilayerperceptronneuralnetworkperformanceanalysiswithapplicationofsolarirradianceforecasting AT mohamedlouzazni novelcooperativemultiinputmultilayerperceptronneuralnetworkperformanceanalysiswithapplicationofsolarirradianceforecasting AT parthapratimroy novelcooperativemultiinputmultilayerperceptronneuralnetworkperformanceanalysiswithapplicationofsolarirradianceforecasting |