Machine Learning-Based Management of Electric Vehicles Charging: Towards Highly-Dispersed Fast Chargers
<p dir="ltr">Coordinated charging of electric vehicles (EVs) improves the overall efficiency of the power grid as it avoids distribution system overloads, increases power quality, and decreases voltage fluctuations. Moreover, the coordinated charging supports flattening the load prof...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , |
| منشور في: |
2020
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| الموضوعات: | |
| الوسوم: |
إضافة وسم
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| _version_ | 1864513511810400256 |
|---|---|
| author | Mostafa Shibl (18810412) |
| author2 | Loay Ismail (18810415) Ahmed Massoud (16875996) |
| author2_role | author author |
| author_facet | Mostafa Shibl (18810412) Loay Ismail (18810415) Ahmed Massoud (16875996) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mostafa Shibl (18810412) Loay Ismail (18810415) Ahmed Massoud (16875996) |
| dc.date.none.fl_str_mv | 2020-10-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/en13205429 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Machine_Learning-Based_Management_of_Electric_Vehicles_Charging_Towards_Highly-Dispersed_Fast_Chargers/26021005 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Automotive engineering Electrical engineering Electronics, sensors and digital hardware Information and computing sciences Data management and data science Machine learning coordinated electric vehicles charging cyber-physical systems (CPSs) Decision Tree (DT) Deep Neural Network (DNN) lectric vehicles charging stations (EVCS) K-Nearest Neighbors (KNN) Long Short-Term Memory (LSTM) machine learning (ML) Naïve Bayes (NB) power rating (PR) Random Forest (RF) Recurrent Neural Networks (RNN) smart grid Support Vector Machine (SVM) |
| dc.title.none.fl_str_mv | Machine Learning-Based Management of Electric Vehicles Charging: Towards Highly-Dispersed Fast Chargers |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Coordinated charging of electric vehicles (EVs) improves the overall efficiency of the power grid as it avoids distribution system overloads, increases power quality, and decreases voltage fluctuations. Moreover, the coordinated charging supports flattening the load profile. Therefore, an effective coordination technique is crucial for the protection of the distribution grid and its components. The substantial power used through charging EVs has undeniable negative impacts on the power grid. Additionally, with the increasing use of EVs, an effective solution for the coordination of EVs charging, particularly when considering the anticipated proliferation of EV fast chargers, is imminently required. In this paper, different machine learning (ML) approaches are compared for the coordination of EVs charging. The ML models can predict the power to be used in EVs charging stations (EVCS). Due to its ability to use historical data to learn and identify patterns for making future decisions with minimal user intervention, ML has been utilized. ML models used in this paper are (1) Decision Tree (DT), (2) Random Forest (RF), (3) Support Vector Machine (SVM), (4) Naïve Bayes (NB), (5) K-Nearest Neighbors (KNN), (6) Deep Neural Networks (DNN), and (7) Long Short-Term Memory (LSTM). These approaches are chosen as they are classifiers known to have the leading results for multiclass classification problems. The results found shed insight on the importance of the techniques used and their high potential in providing a reliable solution for the coordinated charging of EVs, thus improving the performance of the power grid, and reducing power losses and voltage fluctuations. The use of ML provides a less complex method to coordinate EVs, in comparison with conventional optimization techniques such as quadratic programming, and the use of ML is faster as it requires less computational power. LSTM provided the best results with an accuracy of 95% for predicting the most appropriate power rating (PR) for EVCS, followed by RF, DT, DNN, SVM, KNN, and NB. Additionally, LSTM was also the model with the smallest error rate, at a value of ±0.7%, followed by RF, DT, KNN, SVM, DNN, and NB. The results obtained from the LSTM model were similar to the results obtained from past literature using quadratic programming, with the increased speed and simplicity of ML.</p><h2>Other Information</h2><p dir="ltr">Published in: Energies<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/en13205429" target="_blank">https://dx.doi.org/10.3390/en13205429</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_4898985036a0bd1064cc7d2adf0c4b1d |
| identifier_str_mv | 10.3390/en13205429 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26021005 |
| publishDate | 2020 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Machine Learning-Based Management of Electric Vehicles Charging: Towards Highly-Dispersed Fast ChargersMostafa Shibl (18810412)Loay Ismail (18810415)Ahmed Massoud (16875996)EngineeringAutomotive engineeringElectrical engineeringElectronics, sensors and digital hardwareInformation and computing sciencesData management and data scienceMachine learningcoordinated electric vehicles chargingcyber-physical systems (CPSs)Decision Tree (DT)Deep Neural Network (DNN)lectric vehicles charging stations (EVCS)K-Nearest Neighbors (KNN)Long Short-Term Memory (LSTM)machine learning (ML)Naïve Bayes (NB)power rating (PR)Random Forest (RF)Recurrent Neural Networks (RNN)smart gridSupport Vector Machine (SVM)<p dir="ltr">Coordinated charging of electric vehicles (EVs) improves the overall efficiency of the power grid as it avoids distribution system overloads, increases power quality, and decreases voltage fluctuations. Moreover, the coordinated charging supports flattening the load profile. Therefore, an effective coordination technique is crucial for the protection of the distribution grid and its components. The substantial power used through charging EVs has undeniable negative impacts on the power grid. Additionally, with the increasing use of EVs, an effective solution for the coordination of EVs charging, particularly when considering the anticipated proliferation of EV fast chargers, is imminently required. In this paper, different machine learning (ML) approaches are compared for the coordination of EVs charging. The ML models can predict the power to be used in EVs charging stations (EVCS). Due to its ability to use historical data to learn and identify patterns for making future decisions with minimal user intervention, ML has been utilized. ML models used in this paper are (1) Decision Tree (DT), (2) Random Forest (RF), (3) Support Vector Machine (SVM), (4) Naïve Bayes (NB), (5) K-Nearest Neighbors (KNN), (6) Deep Neural Networks (DNN), and (7) Long Short-Term Memory (LSTM). These approaches are chosen as they are classifiers known to have the leading results for multiclass classification problems. The results found shed insight on the importance of the techniques used and their high potential in providing a reliable solution for the coordinated charging of EVs, thus improving the performance of the power grid, and reducing power losses and voltage fluctuations. The use of ML provides a less complex method to coordinate EVs, in comparison with conventional optimization techniques such as quadratic programming, and the use of ML is faster as it requires less computational power. LSTM provided the best results with an accuracy of 95% for predicting the most appropriate power rating (PR) for EVCS, followed by RF, DT, DNN, SVM, KNN, and NB. Additionally, LSTM was also the model with the smallest error rate, at a value of ±0.7%, followed by RF, DT, KNN, SVM, DNN, and NB. The results obtained from the LSTM model were similar to the results obtained from past literature using quadratic programming, with the increased speed and simplicity of ML.</p><h2>Other Information</h2><p dir="ltr">Published in: Energies<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/en13205429" target="_blank">https://dx.doi.org/10.3390/en13205429</a></p>2020-10-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/en13205429https://figshare.com/articles/journal_contribution/Machine_Learning-Based_Management_of_Electric_Vehicles_Charging_Towards_Highly-Dispersed_Fast_Chargers/26021005CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/260210052020-10-01T00:00:00Z |
| spellingShingle | Machine Learning-Based Management of Electric Vehicles Charging: Towards Highly-Dispersed Fast Chargers Mostafa Shibl (18810412) Engineering Automotive engineering Electrical engineering Electronics, sensors and digital hardware Information and computing sciences Data management and data science Machine learning coordinated electric vehicles charging cyber-physical systems (CPSs) Decision Tree (DT) Deep Neural Network (DNN) lectric vehicles charging stations (EVCS) K-Nearest Neighbors (KNN) Long Short-Term Memory (LSTM) machine learning (ML) Naïve Bayes (NB) power rating (PR) Random Forest (RF) Recurrent Neural Networks (RNN) smart grid Support Vector Machine (SVM) |
| status_str | publishedVersion |
| title | Machine Learning-Based Management of Electric Vehicles Charging: Towards Highly-Dispersed Fast Chargers |
| title_full | Machine Learning-Based Management of Electric Vehicles Charging: Towards Highly-Dispersed Fast Chargers |
| title_fullStr | Machine Learning-Based Management of Electric Vehicles Charging: Towards Highly-Dispersed Fast Chargers |
| title_full_unstemmed | Machine Learning-Based Management of Electric Vehicles Charging: Towards Highly-Dispersed Fast Chargers |
| title_short | Machine Learning-Based Management of Electric Vehicles Charging: Towards Highly-Dispersed Fast Chargers |
| title_sort | Machine Learning-Based Management of Electric Vehicles Charging: Towards Highly-Dispersed Fast Chargers |
| topic | Engineering Automotive engineering Electrical engineering Electronics, sensors and digital hardware Information and computing sciences Data management and data science Machine learning coordinated electric vehicles charging cyber-physical systems (CPSs) Decision Tree (DT) Deep Neural Network (DNN) lectric vehicles charging stations (EVCS) K-Nearest Neighbors (KNN) Long Short-Term Memory (LSTM) machine learning (ML) Naïve Bayes (NB) power rating (PR) Random Forest (RF) Recurrent Neural Networks (RNN) smart grid Support Vector Machine (SVM) |