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Compared to the initial baseline timeseries EV charging forecasting models, which performed poorly, the proposed strategy consists of a step-by-step forecasting methodology using data from predictions of road traffic, weather\/seasonality, user charging information, and EV charging load. For each target, various novel implementations and models were conducted, including zero inflated (ZI) and a novel logarithmic ZI (LogZI) regression method.\u00a0Results comparison indicated an increasing accuracy of EV charging demand forecasts from 78.1 to 88.7% regarding <jats:inline-formula>\n              <jats:alternatives>\n                <jats:tex-math>$$R^2$$<\/jats:tex-math>\n                <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msup>\n                    <mml:mi>R<\/mml:mi>\n                    <mml:mn>2<\/mml:mn>\n                  <\/mml:msup>\n                <\/mml:math>\n              <\/jats:alternatives>\n            <\/jats:inline-formula>. It also highlighted a decreasing error from 1.75 to 1.05 kWh regarding MAE and from 2.48 to 1.77 kWh regarding RMSE (CVRMSE and NRMSE were also provided).\u00a0This research contributes a novel one step ahead EV charging demand forecasting framework integrating traffic and user charging predictions with the proposed LogZI regression approach. The proposed, updated version of the ZI approach, methodology performs significantly better compared to the baseline models, aiming to enhance grid stability and optimize charging infrastructure.<\/jats:p>","DOI":"10.1007\/s13042-025-02643-8","type":"journal-article","created":{"date-parts":[[2025,5,2]],"date-time":"2025-05-02T08:37:53Z","timestamp":1746175073000},"page":"6737-6763","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["EV charging forecasting exploiting traffic, weather and user information"],"prefix":"10.1007","volume":"16","author":[{"given":"Aristeidis","family":"Mystakidis","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikolaos","family":"Tsalikidis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paraskevas","family":"Koukaras","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Georgios","family":"Skaltsis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dimosthenis","family":"Ioannidis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christos","family":"Tjortjis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dimitrios","family":"Tzovaras","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,4,30]]},"reference":[{"key":"2643_CR1","doi-asserted-by":"publisher","DOI":"10.3390\/en14082233","author":"Y Amara-Ouali","year":"2021","unstructured":"Amara-Ouali Y, Goude Y, Massart P, Poggi JM, Yan H (2021) A review of electric vehicle load open data and models. 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