Biogeography-based optimization of artificial neural network (BBO-ANN) for solar radiation forecasting
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Date
2023
Journal Title
Journal ISSN
Volume Title
Publisher
Applied Artificial Intelligence
Abstract
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.