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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| مؤلفون آخرون: | , , , , , |
| منشور في: |
2021
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| _version_ | 1864513562736590848 |
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| author | Mohamed Massaoudi (16888710) |
| author2 | Ines Chihi (16888713) Lilia Sidhom (16896387) Mohamed Trabelsi (16869891) Shady S. Refaat (16864269) Haitham Abu-Rub (16855500) Fakhreddine S. Oueslati (16888716) |
| author2_role | author author author author author author |
| author_facet | Mohamed Massaoudi (16888710) Ines Chihi (16888713) Lilia Sidhom (16896387) Mohamed Trabelsi (16869891) Shady S. Refaat (16864269) Haitham Abu-Rub (16855500) Fakhreddine S. Oueslati (16888716) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mohamed Massaoudi (16888710) Ines Chihi (16888713) Lilia Sidhom (16896387) Mohamed Trabelsi (16869891) Shady S. Refaat (16864269) Haitham Abu-Rub (16855500) Fakhreddine S. Oueslati (16888716) |
| dc.date.none.fl_str_mv | 2021-02-26T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2021.3062776 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/An_Effective_Hybrid_NARX-LSTM_Model_for_Point_and_Interval_PV_Power_Forecasting/24049272 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | 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 |
| dc.title.none.fl_str_mv | An Effective Hybrid NARX-LSTM Model for Point and Interval PV Power Forecasting |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <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 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.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2021.3062776" target="_blank">https://dx.doi.org/10.1109/access.2021.3062776</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_3cf0b780d0702cf9aa0dc1d427773651 |
| identifier_str_mv | 10.1109/access.2021.3062776 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24049272 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | An Effective Hybrid NARX-LSTM Model for Point and Interval PV Power ForecastingMohamed Massaoudi (16888710)Ines Chihi (16888713)Lilia Sidhom (16896387)Mohamed Trabelsi (16869891)Shady S. Refaat (16864269)Haitham Abu-Rub (16855500)Fakhreddine S. Oueslati (16888716)EngineeringElectrical engineeringInformation and computing sciencesData management and data scienceMachine learningComputational modelingData modelsLong short-term memory (LSTM)ForecastingMeteorologyNonlinear auto-regressive neural networks with exogenous input (NARXNN)Predictive modelsTabu Search Algorithm (TSA)TrainingUncertaintyPhotovoltaic 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 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.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2021.3062776" target="_blank">https://dx.doi.org/10.1109/access.2021.3062776</a></p>2021-02-26T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2021.3062776https://figshare.com/articles/journal_contribution/An_Effective_Hybrid_NARX-LSTM_Model_for_Point_and_Interval_PV_Power_Forecasting/24049272CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240492722021-02-26T00:00:00Z |
| spellingShingle | An Effective Hybrid NARX-LSTM Model for Point and Interval PV Power Forecasting Mohamed Massaoudi (16888710) 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 |
| status_str | publishedVersion |
| title | An Effective Hybrid NARX-LSTM Model for Point and Interval PV Power Forecasting |
| title_full | An Effective Hybrid NARX-LSTM Model for Point and Interval PV Power Forecasting |
| title_fullStr | An Effective Hybrid NARX-LSTM Model for Point and Interval PV Power Forecasting |
| title_full_unstemmed | An Effective Hybrid NARX-LSTM Model for Point and Interval PV Power Forecasting |
| title_short | An Effective Hybrid NARX-LSTM Model for Point and Interval PV Power Forecasting |
| title_sort | An Effective Hybrid NARX-LSTM Model for Point and Interval PV Power Forecasting |
| topic | 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 |