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Factors Impacting Short-Term Load Forecasting of Charging Station to Electric Vehicle
Department of Software Engineering, Daffodil International University, Dhaka 1216, Bangladesh.
Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system. Centre for Advanced Machine Learning and Application (CAMLAs), Dhaka 1229, Bangladesh.
Centre for Advanced Machine Learning and Application (CAMLAs), Dhaka 1229, Bangladesh.
College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China.
2023 (Engelska)Ingår i: Electronics, E-ISSN 2079-9292, Vol. 12, nr 1Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

The rapid growth of electric vehicles (EVs) is likely to endanger the current power system. Forecasting the demand for charging stations is one of the critical issues while mitigating challenges caused by the increased penetration of EVs. Uncovering load-affecting features of the charging station can be beneficial for improving forecasting accuracy. Existing studies mostly forecast electricity demand of charging stations based on load profiling. It is difficult for public EV charging stations to obtain features for load profiling. This paper examines the power demand of two workplace charging stations to address the above-mentioned issue. Eight different types of load-affecting features are discussed in this study without compromising user privacy. We found that the workplace EV charging station exhibits opposite characteristics to the public EV charging station for some factors. Later, the features are used to design the forecasting model. The average accuracy improvement with these features is 42.73% in terms of RMSE. Moreover, the experiments found that summer days are more predictable than winter days. Finally, a state-of-the-art interpretable machine learning technique has been used to identify top contributing features. As the study is conducted on a publicly available dataset and analyzes the root cause of demand change, it can be used as baseline for future research.

Ort, förlag, år, upplaga, sidor
MDPI , 2023. Vol. 12, nr 1
Nationell ämneskategori
Annan elektroteknik och elektronik
Identifikatorer
URN: urn:nbn:se:mdh:diva-61644DOI: 10.3390/electronics12010055ISI: 000910390400001Scopus ID: 2-s2.0-85145851717OAI: oai:DiVA.org:mdh-61644DiVA, id: diva2:1730915
Tillgänglig från: 2023-01-25 Skapad: 2023-01-25 Senast uppdaterad: 2023-02-01Bibliografiskt granskad

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Kabir, Md Alamgir

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