A two-channel deep network based model for improving ultra-short-term prediction of wind power via utilizing multi-source data

Energy, Год журнала: 2023, Номер 283, С. 128510 - 128510

Опубликована: Июль 24, 2023

Язык: Английский

---

Estimating reference crop evapotranspiration using improved convolutional bidirectional long short-term memory network by multi-head attention mechanism in the four climatic zones of China

Agricultural Water Management, Год журнала: 2024, Номер 292, С. 108665 - 108665

Опубликована: Янв. 9, 2024

Accurate reference crop evapotranspiration (ET0) estimation is essential for agricultural water management, productivity, and irrigation systems. As the standard ET0 method, Penman-Monteith equation has been widely recommended worldwide. However, its application still restricted to comprehensive meteorological data deficiency, making exploration of alternative simpler models acceptable highly meaningful. Concerning aforementioned requirement, this study developed novel deep learning model (MA-CNN-BiLSTM), which incorporates Multi-Head Attention mechanism (MA), Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory network (BiLSTM) as intricate relationship processor, feature extractor, regression component, estimate based on radiation-based (Rn-based), humidity-based (RH-based), temperature-based (T-based) input combinations at 600 stations during 1961–2020 throughout China under internal external cross-validation strategies. Besides, through a comparative evaluation among MA-CNN-BiLSTM, CNN-BiLSTM, BiLSTM, LSTM, Multivariate Adaptive Regression Splines (MARS), empirical models, result indicated that MA-CNN-BiLSTM achieved superior precision, with values Determination Coefficient (R2), Nash–Sutcliffe efficiency coefficient (NSE), Relative Root Mean Square Error (RRMSE), (RMSE), Absolute (MAE) ranging 0.877–0.972, 0.844–0.962, 0.129–0.292, 0.294–0.644 mm d−1, 0.244–0.566 d−1 strategy 0.797–0.927, 0.786–0.920, 0.162–0.335, 0.409–0.969 0.294–0.699 strategy. Specifically, Rn-based excelled in temperate continental zone (TCZ) mountain plateau (MPZ), while RH-based yielded best precision others. Furthermore, was by 2.74–106.04% R2, 1.11–120.49% NSE, 1.41–40.27% RRMSE, 1.68–45.53% RMSE, 1.21–38.87% MAE, respectively. In summary, main contribution present proposal LSTM-type (MA-CNN-BiLSTM) cope various data-missing scenarios China, can provide effective support decision-making regional agriculture management.

Язык: Английский

---

Comparison of strategies for multistep-ahead lake water level forecasting using deep learning models

Journal of Cleaner Production, Год журнала: 2024, Номер 444, С. 141228 - 141228

Опубликована: Фев. 13, 2024

Язык: Английский

---

A short-term wind power prediction approach based on an improved dung beetle optimizer algorithm, variational modal decomposition, and deep learning

Computers & Electrical Engineering, Год журнала: 2024, Номер 116, С. 109182 - 109182

Опубликована: Март 16, 2024

Язык: Английский

---

A hybrid prediction model of improved bidirectional long short-term memory network for cooling load based on PCANet and attention mechanism

Energy, Год журнала: 2024, Номер 292, С. 130388 - 130388

Опубликована: Янв. 23, 2024

Язык: Английский

---