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Our model effectively captures comprehensive spatiotemporal correlations on real-world solar power generation datasets and surpasses several existing methods.
Abstract—Under increasing levels of renewable energy source. (RES) penetration, unpredictability and uncertainty are emerging drivers of power imbalances.
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Our model effectively captures comprehensive spatiotemporal correlations on real-world solar power generation datasets and surpasses several existing methods.
The hybrid deep learning method used in the proposed work is CNN-BiLSTM. The major objective of this work is to make accurate and robust forecasting in hybrid ...
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Feb 15, 2024 · In this study, we propose an energy forecasting methodology that leverages transformers as an AI tool to predict energy production from a hybrid ...
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Sep 5, 2022 · A hybrid deep learning (HDL) algorithm (CNN-BiLSTM) is proposed for predicting the output power from the hybrid systems. The HDL method and the ...
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Jan 11, 2023 · In this work, we propose a hybrid method based on the combination of an LSTM neural network and an autoencoder. The LSTM neural network ...
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May 14, 2024 · This study proposes a hybrid approach that combines variational mode decomposition (VMD), whale optimization algorithm (WOA), and long short- ...
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A novel GBDT-BiLSTM hybrid model on improving day-ahead ... - NCBI
www.ncbi.nlm.nih.gov › PMC10499805
Sep 13, 2023 · This paper proposes a novel GBDT-BiLSTM day-ahead PV forecasting model, which leverages the Teacher Forcing mechanism to combine the strong time ...
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May 3, 2023 · However, the performance of solar irradiance forecasting can be further improved by integrating LSTM into a hybrid model (i.e., CNN–LSTM); this ...
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