Day-Ahead Solar Irradiance Forecasting Using CEEMDAN-PCC-BiLSTM Hybrid Model

Short-term solar irradiation prediction is essential for smooth running of various industries, especially for management of flawless electricity generation and distribution.However, solar datasets are rapid, noisy, and non-linear, making standalone models such as LSTM struggle to extract meaningful patterns for GHI forecasting.The unidirectional nature of LSTM restricts learning to past dependencies only, limiting its ability to forecast sudden irradiance drops.Additionally, vanishing gradient issues in long historical data hinder ability of LSTM to capture complex temporal dependencies, leading to suboptimal forecasting performance.To address these challenges, the proposed model leverages deep learning techniques with a feature-refinement approach.Initially, multiple intrinsic mode functions (IMFs) are extracted from the solar irradiance data, each representing different feature sets.A selection criterion is then applied to retain only the most relevant IMFs, which are integrated to form the input feature set.This refined input is used to train a deep learning network, resulting in day-ahead solar irradiance forecasting model.The performance of the model is evaluated at three distinct locations with varying climatic conditions to test its performance consistency.In the proposed model, reduced error rates are observed, which reflects in lower values of MAPE (2.31%), RMSE (1.55 W/m² ), and MAE (1.41 W/m² ) as compared to the standalone LSTM model, which records MAPE (3.23%), RMSE (3.21 W/m² ), and MAE (1.97 W/m² ).Results demonstrate significant improvements over benchmarking method corresponding to more than 90% improvement across all statistical parameters.Also, consistent accuracy across diverse climates highlights robustness of the proposed model which makes it a reliable and versatile solution for practical forecasting applications.