Improving electric vehicle charging forecasting: A hybrid deep learning approach for probabilistic predictions

Autor: Ali Jamali Jahromi, Mohammad Reza Masoudi, Mohammad Mohammadi, Shahabodin Afrasiabi
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: IET Generation, Transmission & Distribution, Vol 18, Iss 21, Pp 3303-3313 (2024)
Druh dokumentu: article
ISSN: 1751-8695
1751-8687
DOI: 10.1049/gtd2.13276
Popis: Abstract Electric vehicles (EVs) have gained significant attention recently. Despite their advantages, challenges in the power grid, such as providing necessary information for optimal operation, persist. High‐precision forecasting techniques are essential to address the nonlinear and complex behavior of EV charging. A hybrid structure based on deep learning, called LSTLNet, has been proposed. LSTLNet combines convolutional neural networks (CNN), gated recurrent neural networks (GRU), attention mechanisms (AM), and automatic regression (AR) models. This combination improves the deterministic forecasting model and addresses the weaknesses of CNN and GRU. Deterministic prediction, which determines only one point of consumption charge, is prone to error. Therefore, probabilistic forecasting, represented as a probability distribution function (PDF) containing comprehensive statistical information, is preferred. A smooth band limit maximum likelihood (SBLM) estimator is used to indirectly predict the PDF from the data. Comparative results with conventional shallow and deep methods for similar time series forecasting demonstrate the superiority of the proposed method for both deterministic and probabilistic forecasting.
Databáze: Directory of Open Access Journals
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