Biogeography-based optimization of artificial neural network (BBO-ANN) for solar radiation forecasting

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dc.contributor.authorYahya, Umar
dc.contributor.authorBansal, Ajay Kumar
dc.contributor.authorAneja, Nagender
dc.contributor.authorDadheech, Pankaj
dc.contributor.authorSangtani, Virendra,Swaroop
dc.date.accessioned2023-05-03T11:41:17Z
dc.date.available2023-05-03T11:41:17Z
dc.date.issued2023
dc.description.abstractRenewable 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.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/1133
dc.language.isoenen_US
dc.publisherApplied Artificial Intelligenceen_US
dc.titleBiogeography-based optimization of artificial neural network (BBO-ANN) for solar radiation forecastingen_US
dc.typeArticleen_US
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