Short Term Forecasting of Solar Irradiance Using Ensemble CNN-BiLSTM-MLP Model Combined with Error Minimization and CEEMDAN Pre-Processing Technique

Solar energy forecasting is necessary due to its variable and fluctuating nature, but it is also a challenge to predict accurately behaviour of solar irradiation. To capture this, the proposed methodology uses an ensemble model combined with error minimization and CEEMDAN Pre-processing technique. In this paper, data of two locations are used to predict short term forecasting of solar irradiation using seven developed models based on the proposed procedure. The use of hourly forecasting, CEEMDAN method, error minimization and ensemble hybrid model enhance the anti-interference capability of all developed model. Four-year data of New Delhi and Ahmedabad is used and sourced from NSRDB website. Out of all the proposed models CEEMDAN-CNN-BiLSTM-MLP with CEEMDAN_IMF_18 configured signal processing approach achieved least average RMSE, n-RMSE and MAE of both locations with values 13.215 W/m² , 7.13% and 8.605 W/m² respectively and have maximum average R² (99.205%). When compared to persistence model, proposed model with this configuration was able to outperform with average percentage improvement 87.63%, 86.78%, 87.17% and 17.875% in terms of 𝑃𝑅𝑀𝑆𝐸, 𝑃𝑛𝑅𝑀𝑆𝐸, 𝑃𝑀𝐴𝐸 and 𝑃𝑅 2 respectively. The proposed model outperforms existing techniques for solar irradiation forecasting, demonstrating greater efficiency and reliability, making it a valuable reference for future performance optimization.