Combining a deep learning model with multivariate empirical mode decomposition for hourly global horizontal irradiance forecasting

Accurate and reliable global horizontal irradiance forecasting is one of the solutions for the associated problems with grid-integrated PV plants. This study proposes a novel hybrid MEMD-PCA-GRU model for an hour ahead of GHI forecasting. The multivariate empirical mode decomposition (MEMD) breaks the multidimensional data into multivariate subseries termed intrinsic mode functions (IMFs). MEMD helps to remove the naturally produced non-stationary and nonlinear deficiencies within the target series and meteorological predictors. A large number of obtained IMFs necessitates the application of a dimensionality reduction technique. Principal component analysis (PCA) is used here to identify the most informative features from a large set of IMFs. Finally, the gated recurrent unit (GRU) is utilized to predict GHI at four places in India. The performance of the proposed model is tested against some hybrid and standalone models. Double decomposition techniques enhanced the GRU per-formance by a minimum % RMSE (% MAE) improvement of 48.38 (24.97). The proposed model reported an average nRMSE (RMSE) of 7.82% (36.85 W/m2) across four locations. The lowest error metrics of the proposed model reflect the relatively stable and good performance compared to studied single-stage and hybrid benchmark models under different climatic conditions.

Automated summary

This study proposes a hybrid MEMD-PCA-GRU model for accurate and reliable global horizontal irradiance (GHI) forecasting. The model utilizes multivariate empirical mode decomposition (MEMD) to remove non-stationary and nonlinear deficiencies within target series and meteorological predictors. Principal component analysis (PCA) is then applied to identify informative features, and the gated recurrent unit (GRU) is utilized for GHI prediction. The proposed model outperforms other hybrid and standalone models, showing stable and good performance across different climatic conditions.