Combined Long Short-Term Memory Network-Based Short-Term Prediction of Solar Irradiance

Abstract

Achieving the highly accurate and generic prediction of solar irradiance is arduous because solar irradiance possesses intermittent randomness and is influenced by meteorological parameters. Therefore, this paper endeavors a new combined long short-term memory network (CLSTMN) with various influence meteorological parameters as inputs. We investigated the proposed predictive model applicability for short-term solar irradiance prediction application and validated it in the real-time metrological dataset. The proposed prediction model is combined and accumulated by various inputs, incurring six individual long short-term memory models to improve solar irradiance prediction accuracy and generalization. Thus, the CLSTMN-based solar irradiance prediction can be generic and overcome the metrological parameters concerning variability. The experimental results ensure good prediction accuracy with minimal evaluation metrics of the proposed CLSTMN for solar irradiance prediction. The RMSE, MAPE, and MSE achieved based on the proposed CLSTMN one-hour-ahead prediction are $7.7729\times {10}^{-04}$ , $8.2479\times {10}^{-05}$ , and $6.0419\times {10}^{-07}$ and for six-hour-ahead prediction are 0.0157, 0.0017, and $2.4627\times {10}^{-04}$ for sunny days, and for cloudy days, the RMSE, MAPE, and MSE achieved based on the proposed CLSTMN one-hour-ahead prediction are $1.2969\times {10}^{-04}$ , $1.6882\times {10}^{-04}$ , and $1.6819\times {10}^{-08}$ and for six-hour-ahead prediction are 0.0176, 0.0043, and $3.0863\times {10}^{-04}$ , respectively. Finally, we investigate the CLSTMN performance effectiveness by comparative analysis with well-known baseline models. The investigative study shows the surpassing prediction performance of the proposed CLSTMN for short-term solar irradiance prediction.