An Effective Hybrid NARX-LSTM Model for Point and Interval PV Power Forecasting

An Effective Hybrid NARX-LSTM Model for Point and Interval PV Power Forecasting

<p>This paper proposes an effective Photovoltaic (PV) Power Forecasting (PVPF) technique based on hierarchical learning combining Nonlinear Auto-Regressive Neural Networks with exogenous input (NARXNN) with Long Short-Term Memory (LSTM) model. First, the NARXNN model acquires the data to gener...

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Bibliographic Details
Main Author: Mohamed Massaoudi (16888710) (author)
Other Authors: Ines Chihi (16888713) (author), Lilia Sidhom (16896387) (author), Mohamed Trabelsi (16869891) (author), Shady S. Refaat (16864269) (author), Haitham Abu-Rub (16855500) (author), Fakhreddine S. Oueslati (16888716) (author)
Published: 2021
Subjects:
Engineering
Electrical engineering
Information and computing sciences
Data management and data science
Machine learning
Computational modeling
Data models
Long short-term memory (LSTM)
Forecasting
Meteorology
Nonlinear auto-regressive neural networks with exogenous input (NARXNN)
Predictive models
Tabu Search Algorithm (TSA)
Training
Uncertainty
Photovoltaic power forecasting
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