An integrated framework of robust local mean decomposition and bidirectional long short-term memory to forecast solar irradiance

Singla, Pradeep; Duhan, Manoj; Saroha, Sumit

doi:10.1080/15435075.2022.2143272

Cited by 9 publications

(1 citation statement)

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“…The LSTM network uses three basic gates: input gate, forget gate, and output gate. LSTM has the ability to learn long-term dependencies from input sequence data [24,25]. Compared with LSTM, GRU usually has fewer parameters, so it can speed up the training time and reduce the computational complexity; GRU's performance in capturing dependencies in long sequences in a short period of time is not comparable to that of LSTM, and GRU is usually easier to train than LSTM, especially when dealing with smaller datasets or limited computational resources.…”

Section: Plos Onementioning

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: Plos Onementioning

confidence: 99%