Analysis of electric vehicle charging demand forecasting model based on Monte Carlo simulation and EMD-BO-LSTM

dc.contributor.authorAkıl, Murat
dc.contributor.authorDokur, Emrah
dc.contributor.authorBayındır, Ramazan
dc.date.accessioned2023-01-18T07:57:32Z
dc.date.available2023-01-18T07:57:32Z
dc.date.issued2022
dc.departmentTeknik Bilimler Meslek Yüksekokulu
dc.description.abstractThe stochastic charging behaviors of Electric Vehicle (EV) users illustrate the negative effects of bulk charging during peak hours on the grid. To overcome this problem, the bulk EV charging demand forecasting approach is investigated using historical EV charge demand dataset and EV driver mobility statictics in this paper. In this model, a Monte Carlo Simulation (MCS) is perfomed that considers the charging behavior of EV users for the generation of EV charging times. Moreover, the EV charging times are combined with the bulk EV demand hybrid forecasting model using decomposition and deep learning time series method. In first stage, the EV demand time series dataset are divided to improve the model performance by empirical mode decomposition (EMD). Then, all decomposed signals are forecasted separately using the Bayesian optimized Long Short-Term Memory LSTM network (BO-LSTM). Finally, to evaluate the model perfomance, the power system analysis using IEEE 33 busbar test system is performed in terms of distribution network power losses, busbar voltage drops and transformer loading conditions.
dc.identifier.doi10.1109/icSmartGrid55722.2022.9848555
dc.identifier.endpage362en_US
dc.identifier.isbn978-166548604-0
dc.identifier.scopusqualityN/A
dc.identifier.startpage356en_US
dc.identifier.urihttps:/dx.doi.org/10.1109/icSmartGrid55722.2022.9848555
dc.identifier.urihttps://hdl.handle.net/20.500.12451/9959
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof10th International Conference on Smart Grid, icSmartGrid 2022
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectDecomposition Methods
dc.subjectDemand Forecasting Model
dc.subjectElectric Vehicles
dc.subjectMonte-Carlo Simulation
dc.subjectShort-Term Forecasting
dc.subjectStochastic Charging Behavior
dc.titleAnalysis of electric vehicle charging demand forecasting model based on Monte Carlo simulation and EMD-BO-LSTM
dc.typeConference Object

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