Very Short-Term Solar Power Forecasting Using a Frequency Incorporated Deep Learning Model

Very Short-Term Solar Power Forecasting Using a Frequency Incorporated Deep Learning Model

This paper aims to forecast solar power in very short horizons to assist in real-time distribution system operations. Popular machine learning methods for time series forecasting are studied, including recurrent neural networks with Long Short-Term Memory (LSTM). Although LSTM networks perform well...

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Autors principals: Hossein Panamtash, Shahrzad Mahdavi, Qun Zhou Sun, Guo-Jun Qi, Hongrui Liu, Aleksandar Dimitrovski
Format: Article
Idioma:English
Publicat: IEEE 2023-01-01
Col·lecció:IEEE Open Access Journal of Power and Energy
Matèries:
Solar forecasting
state frequency model
discrete Fourier transform (DFT)
long short term memory (LSTM)
very short term solar forecasting
predictive voltage control
Accés en línia:https://ieeexplore.ieee.org/document/10179133/

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https://ieeexplore.ieee.org/document/10179133/

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