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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Vydáno v:	IEEE access Ročník 9; s. 1
Hlavní autoři:	Massaoudi, Mohamed, Chihi, Ines, Sidhom, Lilia, Trabelsi, Mohamed, Refaat, Shady S., Abu-Rub, Haitham, Oueslati, Fakhreddine S.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 01.01.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
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Abstract	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 residual error vector. Then, the stacked LSTM model, optimized by Tabu search algorithm, uses the residual error correction associated with the original data to produce a point and interval PVPF. The performance of the proposed PVPF technique was investigated using two real datasets with different scales and locations. The comparative analysis of the NARX-LSTM with twelve existing benchmarks confirms its superiority in terms of accuracy measures. In summary, the proposed NARX-LSTM technique has the following major achievements: 1) Improves the prediction performance of the original LSTM and NARXNN models; 2) Evaluates the uncertainties associated with point forecasts with high accuracy; 3) Provides a high generalization capability for PV systems with different scales. Numerical results of the comparison of the proposed NARX-LSTM method with two real-world PV systems in Australia and USA demonstrate its improved prediction accuracy, outperforming the benchmark approaches with an overall normalized Rooted Mean Squared Error (nRMSE) of 1.98% and 1.33% respectively.
AbstractList	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 residual error vector. Then, the stacked LSTM model, optimized by Tabu search algorithm, uses the residual error correction associated with the original data to produce a point and interval PVPF. The performance of the proposed PVPF technique was investigated using two real datasets with different scales and locations. The comparative analysis of the NARX-LSTM with twelve existing benchmarks confirms its superiority in terms of accuracy measures. In summary, the proposed NARX-LSTM technique has the following major achievements: 1) Improves the prediction performance of the original LSTM and NARXNN models; 2) Evaluates the uncertainties associated with point forecasts with high accuracy; 3) Provides a high generalization capability for PV systems with different scales. Numerical results of the comparison of the proposed NARX-LSTM method with two real-world PV systems in Australia and USA demonstrate its improved prediction accuracy, outperforming the benchmark approaches with an overall normalized Rooted Mean Squared Error (nRMSE) of 1.98% and 1.33% respectively.
Author	Abu-Rub, Haitham Trabelsi, Mohamed Sidhom, Lilia Oueslati, Fakhreddine S. Chihi, Ines Massaoudi, Mohamed Refaat, Shady S.
Author_xml	– sequence: 1 givenname: Mohamed surname: Massaoudi fullname: Massaoudi, Mohamed organization: Department of Electrical and Computer Engineering, Texas A&M University at Qatar, Doha, Qatar and Laboratoire matériaux molécules et applications (LMMA) à l'IPEST, Carthage University, Tunis, Tunisia. (e-mail: mohamedsadeg_123@hotmail.com) – sequence: 2 givenname: Ines surname: Chihi fullname: Chihi, Ines organization: Laboratory of Energy Applications and Renewable Energy Efficiency (LAPER), El Manar University, Tunisia – sequence: 3 givenname: Lilia surname: Sidhom fullname: Sidhom, Lilia organization: Laboratory of Energy Applications and Renewable Energy Efficiency (LAPER), El Manar University, Tunisia – sequence: 4 givenname: Mohamed surname: Trabelsi fullname: Trabelsi, Mohamed organization: Department of Electronic and Communications Engineering, Kuwait College of Science and Technology, Kuwait – sequence: 5 givenname: Shady S. surname: Refaat fullname: Refaat, Shady S. organization: Department of Electrical and Computer Engineering, Texas A&M University at Qatar, Doha, Qatar – sequence: 6 givenname: Haitham surname: Abu-Rub fullname: Abu-Rub, Haitham organization: Department of Electrical and Computer Engineering, Texas A&M University at Qatar, Doha, Qatar – sequence: 7 givenname: Fakhreddine S. surname: Oueslati fullname: Oueslati, Fakhreddine S. organization: Laboratoire matériaux molécules et applications (LMMA) à l'IPEST, Carthage University, Tunis, Tunisia
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SubjectTerms	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
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Title	An Effective Hybrid NARX-LSTM Model for Point and Interval PV Power Forecasting
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