Biogeography-based Optimization of Artificial Neural Network (BBO-ANN) for Solar Radiation Forecasting
Renewable energy can help India’s economy and society. Solar energy is everywhere and can be used anywhere, making it popular. Solar energy’s drawbacks are weather and environmental dependencies and solar radiation variations. Solar Radiation Forecasting (SRF) reduces this drawback. SRF eliminates s...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | http://dx.doi.org/10.1080/08839514.2023.2166705 |
| _version_ | 1797684795218591744 |
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| author | Ajay Kumar Bansal Virendra Swaroop Sangtani Pankaj Dadheech Nagender Aneja Umar Yahya |
| author_facet | Ajay Kumar Bansal Virendra Swaroop Sangtani Pankaj Dadheech Nagender Aneja Umar Yahya |
| author_sort | Ajay Kumar Bansal |
| collection | DOAJ |
| description | Renewable energy can help India’s economy and society. Solar energy is everywhere and can be used anywhere, making it popular. Solar energy’s drawbacks are weather and environmental dependencies and solar radiation variations. Solar Radiation Forecasting (SRF) reduces this drawback. SRF eliminates solar power generation variations, grid overvoltage, reverse current, and islanding. Short-term solar radiation forecasts improve photovoltaic (PV) power generation and grid connection. Previous promising SRF studies often fail to generalize to new data. A biogeography-based optimization artificial neural network (BBO-ANN) model for SRF is proposed in this work. 5-year and 6-year data are used to train and validate the model. The data was collected from India’s Jaipur Rajasthan weather station from 2014 to 2019. This work used biogeography-based optimization (BBO) to optimize and adjust the inertia weight of artificial neural networks (ANN) during training. The BBO-ANN model developed in this study had a Mean Absolute Percentage Error (MAPE) of 3.55%, which is promising compared to previous SRF studies. The BBO-ANN SRF model introduced in this work can generalize well to new data because it was able to produce equally accurate autumn and winter forecasts despite the great climatic variation that occurs during the summer and spring. |
| first_indexed | 2024-03-12T00:34:58Z |
| format | Article |
| id | doaj.art-34d98530039f45bf8a42aef1802206ce |
| institution | Directory Open Access Journal |
| issn | 0883-9514 1087-6545 |
| language | English |
| last_indexed | 2024-03-12T00:34:58Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj.art-34d98530039f45bf8a42aef1802206ce2023-09-15T10:01:05ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452023-12-0137110.1080/08839514.2023.21667052166705Biogeography-based Optimization of Artificial Neural Network (BBO-ANN) for Solar Radiation ForecastingAjay Kumar Bansal0Virendra Swaroop Sangtani1Pankaj Dadheech2Nagender Aneja3Umar Yahya4Central University of HaryanaManagement and GramothanManagement and GramothanUniverisiti Brunei DarussalamIslamic University in UgandaRenewable energy can help India’s economy and society. Solar energy is everywhere and can be used anywhere, making it popular. Solar energy’s drawbacks are weather and environmental dependencies and solar radiation variations. Solar Radiation Forecasting (SRF) reduces this drawback. SRF eliminates solar power generation variations, grid overvoltage, reverse current, and islanding. Short-term solar radiation forecasts improve photovoltaic (PV) power generation and grid connection. Previous promising SRF studies often fail to generalize to new data. A biogeography-based optimization artificial neural network (BBO-ANN) model for SRF is proposed in this work. 5-year and 6-year data are used to train and validate the model. The data was collected from India’s Jaipur Rajasthan weather station from 2014 to 2019. This work used biogeography-based optimization (BBO) to optimize and adjust the inertia weight of artificial neural networks (ANN) during training. The BBO-ANN model developed in this study had a Mean Absolute Percentage Error (MAPE) of 3.55%, which is promising compared to previous SRF studies. The BBO-ANN SRF model introduced in this work can generalize well to new data because it was able to produce equally accurate autumn and winter forecasts despite the great climatic variation that occurs during the summer and spring.http://dx.doi.org/10.1080/08839514.2023.2166705 |
| spellingShingle | Ajay Kumar Bansal Virendra Swaroop Sangtani Pankaj Dadheech Nagender Aneja Umar Yahya Biogeography-based Optimization of Artificial Neural Network (BBO-ANN) for Solar Radiation Forecasting Applied Artificial Intelligence |
| title | Biogeography-based Optimization of Artificial Neural Network (BBO-ANN) for Solar Radiation Forecasting |
| title_full | Biogeography-based Optimization of Artificial Neural Network (BBO-ANN) for Solar Radiation Forecasting |
| title_fullStr | Biogeography-based Optimization of Artificial Neural Network (BBO-ANN) for Solar Radiation Forecasting |
| title_full_unstemmed | Biogeography-based Optimization of Artificial Neural Network (BBO-ANN) for Solar Radiation Forecasting |
| title_short | Biogeography-based Optimization of Artificial Neural Network (BBO-ANN) for Solar Radiation Forecasting |
| title_sort | biogeography based optimization of artificial neural network bbo ann for solar radiation forecasting |
| url | http://dx.doi.org/10.1080/08839514.2023.2166705 |
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