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

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 generate a res...

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Veröffentlicht in:	IEEE access Jg. 9; S. 1
Hauptverfasser:	Massaoudi, Mohamed, Chihi, Ines, Sidhom, Lilia, Trabelsi, Mohamed, Refaat, Shady S., Abu-Rub, Haitham, Oueslati, Fakhreddine S.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.01.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accuracy Benchmarks Computational modeling Data acquisition Data models Error correction Forecasting Long Short-Term Memory (LSTM) Mathematical models Meteorology Neural networks Nonlinear Auto-Regressive Neural Networks with exogenous input (NARXNN) Photovoltaic cells photovoltaic power forecasting Predictive models Regression analysis Search algorithms Tabu search Tabu Search Algorithm (TSA) Training Uncertainty
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
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