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Neural-network modeling of solar radiation and temperature variability due to climate change in Ibadan Metropolis
Abstract
his research focused on studying the variability of solar radiation and temperature under climate change in the Ibadan metropolis. In the study, spatial distribution, temporal variations, annual distribution, estimation and prediction of the solar radiation, minimum and maximum temperature data in the Ibadan Metropolis was collected. A Long Short-Term Memory Neural Network (LSTM-NN) model was developed for the prediction using the time-series data obtained. An ARIMA model was further developed to compare and validate the LSTM-NN model. The performance of the prediction models were determined using the root mean square error (RMSE) and the mean absolute percentage error (MAPE). The RMSE values for the minimum, maximum temperature and solar radiation predictions were 1.543, 1.290, 1.967, and 1.611, 1.309, 2.106 for the LSTM-NN and ARIMA models respectively, while the MAPE values for the minimum, maximum temperature and solar radiation predictions were 3.603, 4.351, 8.859, and 3.840, 4.480, 9.502 for the LSTM-NN and ARIMA models respectively. The LSTM-NN model had a better prediction performance in all categories with lower error when compared with ARIMA. From the prediction, it was observed that there will be a reduction in the maximum temperature, minimum temperature and solar radiation values when compared to obtained data. The observed minimum temperature ranged from 22.9032-23.2032(0C), while the predicted minimum temperature ranged from 19.9260- 19.977(0C) also the observed maximum temperature ranged from 32.87096-33.7064(0C), while the predicted maximum temperature ranged from 29.5159-29.5529(0C), the observed solar radiation ranged from 19.203-19.722 (W/m2 ), while the predicted solar radiation ranged from 14.123-14.115 (W/m2 ). The year with the highest solar radiation which constitutes the useful energy is 2024 with an average value of 14.1395 W/m2
