An Optimized Hybrid Framework Based on Long-Short Term Memory Neural Networks and Fourier SynchroSqueezed Transform for Photovoltaic Power Forecasting

An Optimized Hybrid Framework Based on Long-Short Term Memory Neural Networks and Fourier SynchroSqueezed Transform for Photovoltaic Power Forecasting

Accurate prediction of photovoltaic power is crucial to optimize its integration into the power grid. However, this task is highly complex due to the inherently stochastic nature of photovoltaic power generation. To address this challenge, this paper proposes a novel hybrid framework that combines a...

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Bibliographic Details
Main Authors: Samer Rajah, Francisco J. Munoz, Alejandro Rodriguez
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Deep learning
long short-term memory neural network
Fourier SynchroSqueezed transform
Bayesian optimization
photovoltaic power prediction
Online Access:https://ieeexplore.ieee.org/document/11080022/
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https://ieeexplore.ieee.org/document/11080022/

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