A Multistage Hybrid Model ICEEMDAN-SE-VMD-RDPG for a Multivariate Solar Irradiance Forecasting

Abstract

Accuracy of solar irradiance forecasting is imperative for the effective utilization and integration of solar energy into the power system. To forecast global horizontal irradiance based on a multivariate meteorological data; this study first evaluates five standalone models, including recurrent deterministic policy gradient (RDPG), long short term memory (LSTM) neural network, extreme gradient boosting (XGB), Gaussian process regression (GPR), and support vector regression (SVR). The RDPG model outperforms the standalone counterparts by demonstrating 2.485 W/m 2 , 20.591 W/m 2 , 18.316 W/m 2 , and 23.176 W/m 2 reductions in RMSE compared to the LSTM, XGB, GPR, and SVR models. Afterward, the performance of the RDPG model is further enhanced, by developing two-stage hybrid models, including improved complete ensemble empirical mode decomposition with additive noise-RDPG (ICEEMDAN-RDPG) and variational mode decomposition-RDPG (VMD-RDPG). The subsequent construction of the hybrid model ICEEMDAN-SE-VMD-RDPG (ISVR) results in the further improvement of the two-stage hybrid models. The ISVR surpasses all the established models including VMD-RDPG, ICEEMDAN-RDPG, RDPG, LSTM, XGB, GPR, and SVR by displaying 18.401 W/m 2 , 13.908 W/m 2 , 33.223 W/m 2 , 53.111 W/m 2 , 67.704 W/m 2 , 67.502 W/m 2 and 69.943 W/m 2 , respectively, decrease in RMSE. The overall results are enlightening and reveal the extension feasibility of the IVSR model for the other forecasting tasks, e.g., renewable energy integration and electrical load forecasting. INDEX TERMS Solar irradiance forecasting, decomposition techniques, sample entropy, correlation, deep reinforcement learning.

HIFA: Promising Heterogeneous Solar Irradiance Forecasting Approach Based on Kernel Mapping

“…0: Divide SI data into training and testing sets. 0: Structure SI data as described in (24), and then normalize SI data using (25). 0: while i ≤ n do end To assess the accuracy of HIFA, we use the root mean square error (RMSE) and mean absolute error (MAE), formulated as follows [35]: where N s , SI P and SI E are the numbers of samples, the predicted and observed values of solar irradiance, respectively.…”

“…In [24], a k-means technique has been employed to split training sets into many groups, and then GRU has been used with each group. The authors of [25] have assessed 5 standalone models, including recurrent deterministic policy gradient, LSTM, Gaussian process regression, extreme gradient boosting, and support vector regression for solar irradiance forecasting while proposing an improved ensemble method. The study of [26] has adopted a multitask learning method to perform a multi-time scale forecast to enhance the accuracy rate as well as the computational efficiency.…”

Long-Term Interbank Bond Rate Prediction Based on ICEEMDAN and Machine Learning

“…Zhang et al proposed to construct a mixed prediction model of EEMD and LSTM in the prediction of surface temperature, and the empirical results show that the prediction effect of this model is better than that of macshine learning prediction models such as Recurrent Neural Network (RNN), LSTM, and EMD-RNN. [32] developed a two-stage hybrid model, including ICEEMDAN-recurrent deterministic policy gradient (RDPG) and variational mode decomposition-RDPG, to forecast the solar irradiance. [33] proposed a hybrid influent forecasting model based on multimodal and ensemble-based deep learning, showing a good performance in predicting the long-term and short-term loads of circuits.…”

A Novel Noise Suppression and Artifact Removal Method of Mechanomyography Based on RLS, IGWO-VMD, and CEEMDAN

“…However, Lahmiri and Boukadoum [30] found that it is not safe to use VMD alone for denoising. To solve the problems caused by using CEEM-DAN and VMD alone, some researchers have used EMDrelated methods in combination with VMD methods in recent years and achieved better results [31][32][33][34]. From these literatures, it can be seen that the combination of the two methods can significantly improve the decomposition process and has a better noise suppression effect than a single one.…”