Deep hybrid CNN–biLSTM model for accurate solar photovoltaic power forecasting: A comparative study with classical and neural models

Accurate short-term forecasting of solar photovoltaic (PV) power is essential for grid stability and renewable energy integration, but remains challenging due to the inherent variability and intermittency of solar generation. This paper introduces a hybrid model that combines a Convolutional Neural...

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Hlavní autoři:	Mohamed Nadour, Abdelhalim Rabehi, Nadji Hadroug, Mawloud Guermoui, Imad Eddine Tibermacine, Abdullah K. Alanazi, Mustapha Habib, Abdelaziz Rabehi
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	Elsevier 2026-06-01
Edice:	Energy Reports
Témata:	Artificial neural networks Bidirectional LSTM Convolutional neural network Deep learning Solar power forecasting Renewable energy prediction
On-line přístup:	http://www.sciencedirect.com/science/article/pii/S2352484726000880
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