Comparative Assessment of Machine Learning Regression Methods for Power System State Estimation

Bodda, Sivaramarao; Agnihotri, Prashant

doi:10.1007/978-981-16-1978-6_34

Cited by 1 publication

(1 citation statement)

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“…A K-Nearest Neighbors (KNN) model is chosen to perform the classification task given only a limited amount of features as input [6]. Although the KNN has already been applied in literature [6], [7], [8], previous studies on EVs charging sessions use the KNN as a regressive prediction model; on the contrary, the present study proposes a KNN Classification [9], [10] model for classifying charging events in different duration categories based on temporal features. Moreover, the majority of the studies in the literature focus more on the energy demand prediction than on the pure charging duration prediction based on user behavior.…”

Section: Introductionmentioning

confidence: 99%

Electric Vehicles charging sessions classification technique for optimized battery charge based on machine learning

Matrone,

Ogliari,

Nespoli

et al. 2023

IEEE Access

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The fast increase in electric vehicle (EV) usage in the last 10 years has raised the need to properly forecast their energy consumption during charge. Lithium-ion batteries have become the major storage component for electric vehicles, avoiding their overcharge can preserve their health and prolong their lifetime. This paper proposes a Machine Learning model based on the K-Nearest Neighbors classification algorithm for EV charging session duration forecast. The model forecasts the duration of the charge by assigning the event to its correct class. Each class contains the charging events whose duration is comprised of a certain interval. The only information used by the algorithm is the one available at the beginning of the charging event (arrival time, starting SOC, calendar data). The model is validated on a real-world dataset containing records of charging sessions from more than 100 users, a sensitivity analysis is performed to assess the impact of different information given as input. The effectiveness of the model with respect to the benchmark models is demonstrated with an increase in performance.

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