Bayesian Optimization Algorithm for ConvLSTM-AE model To Forecast Solar Irradiation
Boussetta, Mohammed Bendali, Wadie Morad, Youssef Motahhir, Saad Krichen, Moez Mian Qaisar, Saeed
Boussetta, Mohammed
Bendali, Wadie
Morad, Youssef
Motahhir, Saad
Krichen, Moez
Mian Qaisar, Saeed
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Supervisor
Date
2024-03-21
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Abstract
As much as the development of smart grid systems, accurate and reliable solar irradiance is crucial. In smart grid systems, forecasting solar irradiance remains a vital solution to mitigate the best management of energy.This study presents a new approach using a hybrid deep learning model, Conv-LSTM-AE, enhanced by Bayesian algorithm optimization. In four seasons of the years, the model was evaluated based on the accuracy using different metrices errors, and time computing in training. The model exhibits exceptional adeptness as compared to other deep learning models, showing that the Conv-LSTM-AE with Bayesian optimization is a standout performer with remarkable accuracy. This development represents a substantial step towards utilizing state-of-the-art technology for the efficient use of solar power within intelligent grid systems.
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Effat University