Μέθοδος μηχανικής μάθησης για την πρόβλεψη κατανάλωσης ενέργειας σε ηλεκτρικά οχήματα

Τσιούκης, Ορέστης

A machine learning method for energy consumption forecasting in electric vehicles

Master Thesis

Author

Τσιούκης, Ορέστης

Date

2025-06

Abstract

As the means of transportation we use for our travels over the years, must become more environmental friendly, the movement of electric vehicles is rapidly increasing year by year. For this reason, new needs are being presented for new electric vehicle charging stations in each area of our cities, to serve the energy demand of electric vehicles as best as possible. Due to this need, it is now necessary to predict the energy demand of electric vehicles in terms of time but also in the terms of the amount of the energy they need in total for a specific day and per area. Thus, we, applying modern techniques for modeling and forecasting the demand for electric load, are called upon to re-implement the techniques from the work εργασία (F.Mohammad, D.K.Kang, M.A.Ahmed et al.,IEEE Access, 5 2023), which aims to solve the problem of electricity demand, and to approximate their results. Using public open source datasets, we ultimately predict and approximate with good accuracy the demand for electricity over time in relation to the above reference work. This study contributes to ensure the uninterrupted operation of the electricity grid of an area, minimizing the probability of potentials malfunctions, such as for example a generalized blackout.

Postgraduate Studies Programme

Κυβερνοασφάλεια και Επιστήμη Δεδομένων

Department

Σχολή Τεχνολογιών Πληροφορικής και Επικοινωνιών. Τμήμα Πληροφορικής

Number of pages

Language

Greek

URI

https://dione.lib.unipi.gr/xmlui/handle/unipi/17916

Collections

Τμήμα Πληροφορικής

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Except where otherwise noted, this item's license is described as
Αναφορά Δημιουργού - Παρόμοια Διανομή 3.0 Ελλάδα