Data Driven Methods for EVs Charging Sessions Forecasting
A special issue of Forecasting (ISSN 2571-9394). This special issue belongs to the section "Power and Energy Forecasting".
Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 18490
Special Issue Editors
Interests: evolutionary computation techniques; neural networks; optimization of EM devices; reflectarray antennas; electrical microgrid
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Electric vehicles (EVs) are currently undergoing unprecedented growth in terms of adoption. Due to the large amount of charging power required, this poses great problems for electric networks, which must be able to manage high power volatility and balance energy supply and demand.
To help to manage the network, it is of vital importance to have an accurate forecast of the power required by EVs in the charging process, which, among others, enables smart charging approaches, with the aim of reducing the impact on the electrical grid.
This Special Issue of Forecasting on “Data-Driven Methods for EV Charging Session Forecasting” is intended to disseminate new promising methods and techniques to forecast the output power and energy of charging EVs. In particular, this Special Issue will focus on the development of statistical or machine learning methods for forecasting information related to charging sessions of EVs, such as initial state-of-charge, power required, arrival time, and charging session duration.
Possible topics include but are not limited to the following:
- EV arrival time forecasting;
- EV state-of-charge forecasting;
- EV charging session duration forecasting;
- EV required charging power;
- Integration of electric vehicle forecasting with smart charging;
- Impact of electric vehicle forecasting on electric grid stability and management.
Dr. Alessandro Niccolai
Dr. Alfredo Nespoli
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forecasting is an international peer-reviewed open access quarterly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- electric vehicles
- power forecasting
- state-of-charge
- load forecasting
- smart charging
- energy management system
