A Multiprocessing-Based Sensitivity Analysis of Machine Learning Algorithms for Load Forecasting of Electric Power Distribution System
<p>For the utility to plan the resources accurately and balance the electricity supply and demand, accurate and timely forecasting is required. The proliferation of smart meters in the grids has resulted in an explosion of energy datasets. Processing such data is challenging and usually takes...
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| مؤلفون آخرون: | , , , , , , |
| منشور في: |
2021
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| الموضوعات: | |
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| _version_ | 1864513562715619328 |
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| author | Ameema Zainab (16864263) |
| author2 | Dabeeruddin Syed (16864260) Ali Ghrayeb (16864266) Haitham Abu-Rub (16855500) Shady S. Refaat (16864269) Mahdi Houchati (16891560) Othmane Bouhali (8252544) Santiago Banales Lopez (16896411) |
| author2_role | author author author author author author author |
| author_facet | Ameema Zainab (16864263) Dabeeruddin Syed (16864260) Ali Ghrayeb (16864266) Haitham Abu-Rub (16855500) Shady S. Refaat (16864269) Mahdi Houchati (16891560) Othmane Bouhali (8252544) Santiago Banales Lopez (16896411) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ameema Zainab (16864263) Dabeeruddin Syed (16864260) Ali Ghrayeb (16864266) Haitham Abu-Rub (16855500) Shady S. Refaat (16864269) Mahdi Houchati (16891560) Othmane Bouhali (8252544) Santiago Banales Lopez (16896411) |
| dc.date.none.fl_str_mv | 2021-02-16T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2021.3059730 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_Multiprocessing-Based_Sensitivity_Analysis_of_Machine_Learning_Algorithms_for_Load_Forecasting_of_Electric_Power_Distribution_System/24049293 |
| 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 Load modeling Load forecasting Predictive models Forecasting Program processors Computational modeling Parallel processing Big data applications Machine learning algorithms Load forecast Smart grids |
| dc.title.none.fl_str_mv | A Multiprocessing-Based Sensitivity Analysis of Machine Learning Algorithms for Load Forecasting of Electric Power Distribution System |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>For the utility to plan the resources accurately and balance the electricity supply and demand, accurate and timely forecasting is required. The proliferation of smart meters in the grids has resulted in an explosion of energy datasets. Processing such data is challenging and usually takes a longer time than the requirement of a short-term load forecast. The paper addresses this concern by utilizing parallel computing capabilities to minimize the execution time while maintaining highly accurate load forecasting models. In this paper, a thousand smart meter energy datasets are analyzed to perform day ahead, hourly short-term load forecast (STLF). The paper utilizes multi-processing to enhance the overall execution time of the forecasting models by submitting simultaneous jobs to all the processors available. The paper demonstrates the efficacy of the proposed approach through the choice of machine learning (ML) models, execution time, and scalability. The proposed approach is validated on real energy consumption data collected at distribution transformers' level in Spanish Electrical Grid. Decision trees have outperformed the other models accomplishing a tradeoff between model accuracy and execution time. The methodology takes only 4 minutes to train 1,000 transformers for an hourly day-ahead forecast of (~24 million records) utilizing 32 processors.</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.2021.3059730" target="_blank">https://dx.doi.org/10.1109/access.2021.3059730</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_715ca822021603e30bd7c810729d4184 |
| identifier_str_mv | 10.1109/access.2021.3059730 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24049293 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A Multiprocessing-Based Sensitivity Analysis of Machine Learning Algorithms for Load Forecasting of Electric Power Distribution SystemAmeema Zainab (16864263)Dabeeruddin Syed (16864260)Ali Ghrayeb (16864266)Haitham Abu-Rub (16855500)Shady S. Refaat (16864269)Mahdi Houchati (16891560)Othmane Bouhali (8252544)Santiago Banales Lopez (16896411)EngineeringElectrical engineeringInformation and computing sciencesDistributed computing and systems softwareMachine learningLoad modelingLoad forecastingPredictive modelsForecastingProgram processorsComputational modelingParallel processingBig data applicationsMachine learning algorithmsLoad forecastSmart grids<p>For the utility to plan the resources accurately and balance the electricity supply and demand, accurate and timely forecasting is required. The proliferation of smart meters in the grids has resulted in an explosion of energy datasets. Processing such data is challenging and usually takes a longer time than the requirement of a short-term load forecast. The paper addresses this concern by utilizing parallel computing capabilities to minimize the execution time while maintaining highly accurate load forecasting models. In this paper, a thousand smart meter energy datasets are analyzed to perform day ahead, hourly short-term load forecast (STLF). The paper utilizes multi-processing to enhance the overall execution time of the forecasting models by submitting simultaneous jobs to all the processors available. The paper demonstrates the efficacy of the proposed approach through the choice of machine learning (ML) models, execution time, and scalability. The proposed approach is validated on real energy consumption data collected at distribution transformers' level in Spanish Electrical Grid. Decision trees have outperformed the other models accomplishing a tradeoff between model accuracy and execution time. The methodology takes only 4 minutes to train 1,000 transformers for an hourly day-ahead forecast of (~24 million records) utilizing 32 processors.</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.2021.3059730" target="_blank">https://dx.doi.org/10.1109/access.2021.3059730</a></p>2021-02-16T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2021.3059730https://figshare.com/articles/journal_contribution/A_Multiprocessing-Based_Sensitivity_Analysis_of_Machine_Learning_Algorithms_for_Load_Forecasting_of_Electric_Power_Distribution_System/24049293CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240492932021-02-16T00:00:00Z |
| spellingShingle | A Multiprocessing-Based Sensitivity Analysis of Machine Learning Algorithms for Load Forecasting of Electric Power Distribution System Ameema Zainab (16864263) Engineering Electrical engineering Information and computing sciences Distributed computing and systems software Machine learning Load modeling Load forecasting Predictive models Forecasting Program processors Computational modeling Parallel processing Big data applications Machine learning algorithms Load forecast Smart grids |
| status_str | publishedVersion |
| title | A Multiprocessing-Based Sensitivity Analysis of Machine Learning Algorithms for Load Forecasting of Electric Power Distribution System |
| title_full | A Multiprocessing-Based Sensitivity Analysis of Machine Learning Algorithms for Load Forecasting of Electric Power Distribution System |
| title_fullStr | A Multiprocessing-Based Sensitivity Analysis of Machine Learning Algorithms for Load Forecasting of Electric Power Distribution System |
| title_full_unstemmed | A Multiprocessing-Based Sensitivity Analysis of Machine Learning Algorithms for Load Forecasting of Electric Power Distribution System |
| title_short | A Multiprocessing-Based Sensitivity Analysis of Machine Learning Algorithms for Load Forecasting of Electric Power Distribution System |
| title_sort | A Multiprocessing-Based Sensitivity Analysis of Machine Learning Algorithms for Load Forecasting of Electric Power Distribution System |
| topic | Engineering Electrical engineering Information and computing sciences Distributed computing and systems software Machine learning Load modeling Load forecasting Predictive models Forecasting Program processors Computational modeling Parallel processing Big data applications Machine learning algorithms Load forecast Smart grids |