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For the regulation and operation of electrical systems, a reliable and precise prediction of solar irradiation is extremely beneficial. Therefore, this paper deals with the issue of accurate and reliable estimation of solar irradiance by developing and investigating the different standalone and hybrid forecasting models. Eleven different models: Feedforward neural network (FFNN), long short term memory (LSTM), gated recurrent unit (GRU), Bidirectional long short term memory (BDLSTM), BDLSTM with empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), variational mode decomposition (VMD), wavelet transform (WT) and wavelet packet decomposition (WPD) are developed for point and interval forecasting of solar global horizontal irradiance (GHI) on hour ahead basis for two Indian locations. The autocorrelation function ((ACF) and partial autocorrelation function (PACF) are used as statistical measurement to select the optimal lags of the input lags of deep learning (DL) predictors. Likewise, the grid search algorithm is used to select the optimum value of hyperparameters of the predictor. To evaluate the model's performance, root mean square error (RMSE) and mean absolute error (MAE) for point forecast whereas; prediction interval nominal confidence (PINC), average coverage error (ACE) and prediction interval average width (PIAW) for the interval forecast are calculate for annual and seasonal datasets. From the study, the WPD based model achieved the minimum annual RMSE (11.35 W/m2 & 10.49 W/m2) and MAE (6.04 W/m2 & 6.41W/m2). Likewise, at confidence interval (CI) = 99%, the PICP, ACE and PIAW achieved by WPD are ranges from 80.66–81.27, 19.92–21.03, 17.71–18.33 respectively. [ABSTRACT FROM AUTHOR]

In recent years, the penetration of solar power at residential and utility levels has progressed exponentially. However, due to its stochastic nature, the prediction of solar global horizontal irradiance (GHI) with higher accuracy is a challenging task; but, vital for grid management: planning, scheduling & balancing. Therefore, this paper proposes an ensemble model using the extended scope of wavelet transform (WT) and bidirectional long short term memory (BiLSTM) deep learning network to forecast 24-h ahead solar GHI. The WT decomposes the input time series data into different finite intrinsic model functions (IMF) to extract the statistical features of input time series. Further, the study reduces the number of IMF series by combining the wavelet decomposed components (D1-D6) series on the basis of comprehensive experimental analysis with an aim to improve the forecasting accuracy. Next, the trained standalone BiLSTM networks are allocated to each IMF sub-series to execute the forecasting. Finally, the forecasted values of each sub-series from BiLSTM networks are reconstructed to deliver the final solar GHI forecast. The study performed monthly solar GHI forecasting for one year dataset using one month moving window mechanism for the location of Ahmedabad, Gujarat, India. For the performance comparison, the naïve predictor as a benchmark model, standalone long short term memory (LSTM), gated recurrent unit (GRU), BiLSTM and two other wavelet-based BiLSTM models are also simulated. From the results, it is observed that the proposed model outperforms other models in terms of root mean square error (RMSE) & mean absolute percentage error (MAPE), coefficient of determination (R2) and forecast skill (FS). The proposed model reduces the monthly average RMSE by range from 26.04–58.89%, 5.17–31.35%, 23.26–56.06% & 21.08–57% in comparison with benchmark, standalone BiLSTM, GRU & LSTM networks respectively. On the other hand, the monthly average MAPE is reduced by range from 9 to 51.18%, 12.59–28.14%, 30.43–59.19% & 26.54–58.92% in comparison to benchmark, standalone BiLSTM, GRU & LSTM respectively. Further, the proposed model obtained the value of R2 equal to 0.94 and forecast skill (%) of 47% with reference to the benchmark model. [ABSTRACT FROM AUTHOR]