Enhanced Deep Belief Network Based on Ensemble Learning and Tree-Structured of Parzen Estimators: An Optimal Photovoltaic Power Forecasting Method
<p>The random fluctuation and non-uniformity of Photovoltaic (PV) power generation greatly affect the power grids’ stability and operation. This paper addresses the high volatility of PV power by proposing a precise and reliable ensemble learning model for short-term PV power generation foreca...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , , , |
| منشور في: |
2021
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513562697793536 |
|---|---|
| author | Mohamed Massaoudi (16888710) |
| author2 | Haitham Abu-Rub (16855500) Shady S. Refaat (16864269) Mohamed Trabelsi (16869891) Ines Chihi (16888713) Fakhreddine S. Oueslati (16888716) |
| author2_role | author author author author author |
| author_facet | Mohamed Massaoudi (16888710) Haitham Abu-Rub (16855500) Shady S. Refaat (16864269) Mohamed Trabelsi (16869891) Ines Chihi (16888713) Fakhreddine S. Oueslati (16888716) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mohamed Massaoudi (16888710) Haitham Abu-Rub (16855500) Shady S. Refaat (16864269) Mohamed Trabelsi (16869891) Ines Chihi (16888713) Fakhreddine S. Oueslati (16888716) |
| dc.date.none.fl_str_mv | 2021-11-08T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2021.3125895 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Enhanced_Deep_Belief_Network_Based_on_Ensemble_Learning_and_Tree-Structured_of_Parzen_Estimators_An_Optimal_Photovoltaic_Power_Forecasting_Method/24056460 |
| 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 Distributed computing and systems software Machine learning Forecasting Support vector machines Temperature distribution Predictive models Photovoltaic systems Feature extraction Wind forecasting Deep belief network PV power forecasting Stacking ensemble Smart grid Power generation planning |
| dc.title.none.fl_str_mv | Enhanced Deep Belief Network Based on Ensemble Learning and Tree-Structured of Parzen Estimators: An Optimal Photovoltaic Power Forecasting Method |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>The random fluctuation and non-uniformity of Photovoltaic (PV) power generation greatly affect the power grids’ stability and operation. This paper addresses the high volatility of PV power by proposing a precise and reliable ensemble learning model for short-term PV power generation forecasting. The proposed forecasting tool incorporates a base model and meta-model layers. The first-layer base learner combines extreme learning machines, extremely randomized trees, k-nearest neighbor, and mondrian forest models. The meta-model layer exploits deep belief network to generate the final outputs. The hyper-parameters of the proposed stacking ensemble are carefully tuned using the tree-structured of parzen estimators algorithm to achieve top-notch predictive performance. The proposed model is thoroughly assessed through an empirical study using a real data set from Australia. The simulation results confirm the performance superiority of the proposed model over the existing forecasting models with the lowest average root mean square error and mean absolute percentage error of 3.88kW and 2.30%, 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.2021.3125895" target="_blank">https://dx.doi.org/10.1109/access.2021.3125895</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_b64fbbeb9c8f6290013ec597f5d645d7 |
| identifier_str_mv | 10.1109/access.2021.3125895 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24056460 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Enhanced Deep Belief Network Based on Ensemble Learning and Tree-Structured of Parzen Estimators: An Optimal Photovoltaic Power Forecasting MethodMohamed Massaoudi (16888710)Haitham Abu-Rub (16855500)Shady S. Refaat (16864269)Mohamed Trabelsi (16869891)Ines Chihi (16888713)Fakhreddine S. Oueslati (16888716)EngineeringElectrical engineeringInformation and computing sciencesDistributed computing and systems softwareMachine learningForecastingSupport vector machinesTemperature distributionPredictive modelsPhotovoltaic systemsFeature extractionWind forecastingDeep belief networkPV power forecastingStacking ensembleSmart gridPower generation planning<p>The random fluctuation and non-uniformity of Photovoltaic (PV) power generation greatly affect the power grids’ stability and operation. This paper addresses the high volatility of PV power by proposing a precise and reliable ensemble learning model for short-term PV power generation forecasting. The proposed forecasting tool incorporates a base model and meta-model layers. The first-layer base learner combines extreme learning machines, extremely randomized trees, k-nearest neighbor, and mondrian forest models. The meta-model layer exploits deep belief network to generate the final outputs. The hyper-parameters of the proposed stacking ensemble are carefully tuned using the tree-structured of parzen estimators algorithm to achieve top-notch predictive performance. The proposed model is thoroughly assessed through an empirical study using a real data set from Australia. The simulation results confirm the performance superiority of the proposed model over the existing forecasting models with the lowest average root mean square error and mean absolute percentage error of 3.88kW and 2.30%, 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.2021.3125895" target="_blank">https://dx.doi.org/10.1109/access.2021.3125895</a></p>2021-11-08T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2021.3125895https://figshare.com/articles/journal_contribution/Enhanced_Deep_Belief_Network_Based_on_Ensemble_Learning_and_Tree-Structured_of_Parzen_Estimators_An_Optimal_Photovoltaic_Power_Forecasting_Method/24056460CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240564602021-11-08T00:00:00Z |
| spellingShingle | Enhanced Deep Belief Network Based on Ensemble Learning and Tree-Structured of Parzen Estimators: An Optimal Photovoltaic Power Forecasting Method Mohamed Massaoudi (16888710) Engineering Electrical engineering Information and computing sciences Distributed computing and systems software Machine learning Forecasting Support vector machines Temperature distribution Predictive models Photovoltaic systems Feature extraction Wind forecasting Deep belief network PV power forecasting Stacking ensemble Smart grid Power generation planning |
| status_str | publishedVersion |
| title | Enhanced Deep Belief Network Based on Ensemble Learning and Tree-Structured of Parzen Estimators: An Optimal Photovoltaic Power Forecasting Method |
| title_full | Enhanced Deep Belief Network Based on Ensemble Learning and Tree-Structured of Parzen Estimators: An Optimal Photovoltaic Power Forecasting Method |
| title_fullStr | Enhanced Deep Belief Network Based on Ensemble Learning and Tree-Structured of Parzen Estimators: An Optimal Photovoltaic Power Forecasting Method |
| title_full_unstemmed | Enhanced Deep Belief Network Based on Ensemble Learning and Tree-Structured of Parzen Estimators: An Optimal Photovoltaic Power Forecasting Method |
| title_short | Enhanced Deep Belief Network Based on Ensemble Learning and Tree-Structured of Parzen Estimators: An Optimal Photovoltaic Power Forecasting Method |
| title_sort | Enhanced Deep Belief Network Based on Ensemble Learning and Tree-Structured of Parzen Estimators: An Optimal Photovoltaic Power Forecasting Method |
| topic | Engineering Electrical engineering Information and computing sciences Distributed computing and systems software Machine learning Forecasting Support vector machines Temperature distribution Predictive models Photovoltaic systems Feature extraction Wind forecasting Deep belief network PV power forecasting Stacking ensemble Smart grid Power generation planning |