Exploring the Potentials of Artificial Neural Network Trained with Differential Evolution for Estimating Global Solar Radiation
The use of solar powered systems is gradually getting more attention due to technological advances as well as cost effectiveness. Thus, solar powered systems like photovoltaic, concentrated solar power, concentrator photovoltaics, as well as hydrogen production systems are now commercially available...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-05-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/10/2488 |
| _version_ | 1797567955277447168 |
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| author | Olubayo M. Babatunde Josiah L. Munda Yskandar Hamam |
| author_facet | Olubayo M. Babatunde Josiah L. Munda Yskandar Hamam |
| author_sort | Olubayo M. Babatunde |
| collection | DOAJ |
| description | The use of solar powered systems is gradually getting more attention due to technological advances as well as cost effectiveness. Thus, solar powered systems like photovoltaic, concentrated solar power, concentrator photovoltaics, as well as hydrogen production systems are now commercially available for electricity generation. A major input to these systems is solar radiation data which is either partially available or not available in many remote communities. Predictive models can be used in estimating the amount and pattern of solar radiation in any location. This paper presents the use of evolutionary algorithm in improving the generalization capabilities and efficiency of multilayer feed-forward artificial neural network for the prediction of solar radiation using meteorological parameters as input. Meteorological parameters which included monthly average daily of: sunshine hour, solar radiation, maximum temperature and minimum temperature were used in the evaluation. Results show that the proposed model returned a RMSE of 1.1967, NSE of 0.8137 and <inline-formula> <math display="inline"> <semantics> <msup> <mi>R</mi> <mn>2</mn> </msup> </semantics> </math> </inline-formula> of 0.8254. |
| first_indexed | 2024-03-10T19:49:31Z |
| format | Article |
| id | doaj.art-52679b7c11e74f628c316eb8bcf03fda |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T19:49:31Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-52679b7c11e74f628c316eb8bcf03fda2023-11-20T00:31:36ZengMDPI AGEnergies1996-10732020-05-011310248810.3390/en13102488Exploring the Potentials of Artificial Neural Network Trained with Differential Evolution for Estimating Global Solar RadiationOlubayo M. Babatunde0Josiah L. Munda1Yskandar Hamam2Department of Electrical Engineering, French South African Institute of Technology (F’SATI), Tshwane University of Technology, Pretoria 0183, South AfricaDepartment of Electrical Engineering, French South African Institute of Technology (F’SATI), Tshwane University of Technology, Pretoria 0183, South AfricaDepartment of Electrical Engineering, French South African Institute of Technology (F’SATI), Tshwane University of Technology, Pretoria 0183, South AfricaThe use of solar powered systems is gradually getting more attention due to technological advances as well as cost effectiveness. Thus, solar powered systems like photovoltaic, concentrated solar power, concentrator photovoltaics, as well as hydrogen production systems are now commercially available for electricity generation. A major input to these systems is solar radiation data which is either partially available or not available in many remote communities. Predictive models can be used in estimating the amount and pattern of solar radiation in any location. This paper presents the use of evolutionary algorithm in improving the generalization capabilities and efficiency of multilayer feed-forward artificial neural network for the prediction of solar radiation using meteorological parameters as input. Meteorological parameters which included monthly average daily of: sunshine hour, solar radiation, maximum temperature and minimum temperature were used in the evaluation. Results show that the proposed model returned a RMSE of 1.1967, NSE of 0.8137 and <inline-formula> <math display="inline"> <semantics> <msup> <mi>R</mi> <mn>2</mn> </msup> </semantics> </math> </inline-formula> of 0.8254.https://www.mdpi.com/1996-1073/13/10/2488global solar radiationfeed-forward artificial neural networkdifferential evolutionhydrogen productionrenewable energy |
| spellingShingle | Olubayo M. Babatunde Josiah L. Munda Yskandar Hamam Exploring the Potentials of Artificial Neural Network Trained with Differential Evolution for Estimating Global Solar Radiation Energies global solar radiation feed-forward artificial neural network differential evolution hydrogen production renewable energy |
| title | Exploring the Potentials of Artificial Neural Network Trained with Differential Evolution for Estimating Global Solar Radiation |
| title_full | Exploring the Potentials of Artificial Neural Network Trained with Differential Evolution for Estimating Global Solar Radiation |
| title_fullStr | Exploring the Potentials of Artificial Neural Network Trained with Differential Evolution for Estimating Global Solar Radiation |
| title_full_unstemmed | Exploring the Potentials of Artificial Neural Network Trained with Differential Evolution for Estimating Global Solar Radiation |
| title_short | Exploring the Potentials of Artificial Neural Network Trained with Differential Evolution for Estimating Global Solar Radiation |
| title_sort | exploring the potentials of artificial neural network trained with differential evolution for estimating global solar radiation |
| topic | global solar radiation feed-forward artificial neural network differential evolution hydrogen production renewable energy |
| url | https://www.mdpi.com/1996-1073/13/10/2488 |
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