A point and interval forecasting of solar irradiance using different decomposition based hybrid models

Singla, Pradeep; Duhan, Manoj; Saroha, Sumit

doi:10.1007/s12145-023-01020-9

Cited by 11 publications

(1 citation statement)

References 32 publications

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“…In the data processing stage, Recurrent Neural Networks (RNN) may face the challenge of gradient explosion due to the large amount of data and the long duration of the sequence [22,23]. In 1997, Horchreiter and Schmidhuber proposed a special type of Recurrent Neural Network…”

Section: Gru Modelmentioning

confidence: 99%

Short-term prediction of PM2.5 concentration by hybrid neural network based on sequence decomposition

Wu,

Zhu,

Wen

2024

PLoS ONE

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Accurate forecasting of PM2.5 concentrations serves as a critical tool for mitigating air pollution. This study introduces a novel hybrid prediction model, termed MIC-CEEMDAN-CNN-BiGRU, for short-term forecasting of PM2.5 concentrations using a 24-hour historical data window. Utilizing the Maximal Information Coefficient (MIC) for feature selection, the model integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Convolutional Neural Network (CNN), and Bidirectional Recurrent Gated Neural Network (BiGRU) to optimize predictive accuracy. We used 2016 PM2.5 monitoring data from Beijing, China as the empirical basis of this study and compared the model with several deep learning frameworks. RNN, LSTM, GRU, and other hybrid models based on GRU, respectively. The experimental results show that the prediction results of the hybrid model proposed in this question are more accurate than those of other models, and the R2 of the hybrid model proposed in this paper improves the R2 by nearly 5 percentage points compared with that of the single model; reduces the MAE by nearly 5 percentage points; and reduces the RMSE by nearly 11 percentage points. The results show that the hybrid prediction model proposed in this study is more accurate than other models in predicting PM2.5.

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Section: Gru Modelmentioning

confidence: 99%