Multifaceted irradiance prediction by exploiting hybrid decomposition-entropy-Spatiotemporal attention based Sequence2Sequence models

Abstract

Accurate prediction models enable the efficacious utilization and integration of solar energy into the power system. Therefore, this study aims to develop novel hybrid prediction models by employing correlation analysis (CA), decomposition techniques, sample entropy (SE), and spatio-temporal attention (STA) based sequence2sequence (S2S) algorithm for accurate prediction of global horizontal irradiance (GHI). The decomposition techniques include variational mode decomposition (VMD), improved complete ensemble empirical mode decomposition with additive noise (ICEEMDAN), and maximum overlap discrete wavelet transform (MODWT). The VMD-STA-S2S hybrid model surmounts the associated hybrid and standalone prediction models by revealing the highest prediction efficiency and the lowest error. Compared to the SARIMAX, SVR, ANN, XGB, GRU, LSTM, and S2S models, the VMD-STA-S2S model reduced RMSE during testing by 80.927 W/m2, 75.426 W/m2, 73.487 W/m2, 62.394 W/m2, 57.811 W/m2, 52.007 W/m2, and 41.836 W/m2, respectively. Similarly, the reductions in RMSE by VMD-STA-S2S model compared to SA-S2S, TA-S2S, STA-S2S, MODWT-STA-S2S, ICEEMDAN-STA-S2S, ICEEMDAN-SE-STA-S2S, and VMD-SE-STA-S2S models are 24.054 W/m2, 20.951 W/m2, 12.702 W/m2, 15.396 W/m2, 9.921 W/m2, 6.103 W/m2 and 0.484 W/m2, respectively, during testing. Furthermore, considering NSE during testing, the VMD-STA-S2S model is 8.66%, 7.72%, 7.41%, 5.71%, 5.07%, 4.31%, 3.11%, 1.43%, 1.19%, 0.64%, 0.81%, 0.47%, 0.35% and 0.07%, more efficient than the SARIMAX, SVR, ANN, XGB, GRU, LSTM, S2S, SA-S2S, TA-S2S, STA-S2S, MODWT-STA-S2S, ICEEMDAN-STA-S2S, ICEEMDAN-SE-STA-S2S, and VMD-SE-STA-S2S models, respectively. The superior performance of VMD-STA-S2S over its counterparts corroborates the integration of the VMD technique and STA-based S2S algorithm for GHI prediction. The multivariate meteorological data of this study is decomposed by VMD into subcomponents more effectively than the ICEEMDAN and MODWT techniques. VMD decomposed subcomponents are further fed to the STA-S2S to efficiently extract and learn the spatial and temporal features, resulting in the enhanced and superior prediction outcomes of the VMD-STA-S2S model compared to all the counterpart models. Besides GHI prediction, the proposed model is also appropriate for other time-series data, including renewable energy, electrical load, and environment monitoring.