[HTML][HTML] Enhancing wind speed forecasting accuracy using a GWO-nested CEEMDAN-CNN-BiLSTM model
QB Phan, TT Nguyen - ICT Express, 2024 - Elsevier
This study introduces an advanced artificial model, grey wolf optimization (GWO)-nested
complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) …
complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) …
An adaptive method for real‐time photovoltaic power forecasting utilizing mathematics and statistics: Case studies in Australia and Vietnam
T Nguyen‐Duc, H Vu‐Xuan‐Son… - IET Renewable …, 2024 - Wiley Online Library
The advancement of Photovoltaic technology has undergone rapid acceleration in recent
years. Nonetheless, the most significant drawback of Photovoltaic is its intermittence, making …
years. Nonetheless, the most significant drawback of Photovoltaic is its intermittence, making …
Short-term multi-step forecasting of rooftop solar power generation using a combined data decomposition and deep learning model of EEMD-GRU
In this study, an integrated forecasting model was developed by combining the ensemble
empirical mode decomposition (EEMD) model and gated recurrent unit (GRU) neural …
empirical mode decomposition (EEMD) model and gated recurrent unit (GRU) neural …
[PDF][PDF] A hybrid model of decomposition, extended Kalman filter and autoregressive-long short-term memory network for hourly day ahead wind speed forecasting
This research work introduced an innovative forecasting approach that combined the
Autoregressive–Long Short-Term Memory (AR-LSTM) neural network with decomposition …
Autoregressive–Long Short-Term Memory (AR-LSTM) neural network with decomposition …