Harnessing the power of hybrid deep learning algorithm for the estimation of global horizontal irradiance

Abstract

Accurately and precisely estimating global horizontal irradiance (GHI) poses significant challenges due to the unpredictable nature of climate parameters and geographical limitations. To address this challenge, this study proposes a forecasting framework using an integrated model of the convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU). The proposed model uses a dataset of four different districts in Rajasthan, each with unique solar irradiance patterns. Firstly, the data was preprocessed and then trained with the optimized parameters of the standalone and hybrid models and compared. It can be observed that the proposed hybrid model (CNN-LSTM-GRU) consistently outperformed all other models regarding Mean absolute error (MAE) and Root mean squared error (RMSE). The experimental results demonstrate that the proposed method forecasts accurate GHI with a RMSE of 0.00731, 0.00730, 0.00775, 0.00810 and MAE of 0.00516, 0.00524, 0.00552, 0.00592 for Barmer, Jaisalmer, Jodhpur and Bikaner respectively. This indicates that the model is better at minimizing prediction errors and providing more accurate GHI estimates. Additionally, the proposed model achieved a higher coefficient of determination (R (Ghimire et al., 2019)), suggesting that it best fits the dataset. A higher R2 value signifies that the proposed model could explain a significant portion of the variance in the GHI dataset, further emphasizing its predictive capabilities. In conclusion, this work demonstrates the effectiveness of the hybrid algorithm in improving adaptability and enhancing prediction accuracy for GHI estimation.