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A data-driven framework for medium-term electric vehicle charging demand forecasting

Energy and AI 2023年第4期14卷 201-211页

作者： Alexander Orzechowski Loren Lugosch Hao Shu Raymond Yang Wei Li Brett H.Meyer McGill University Mcconnell Engineering Building3380 University StMontreal QC H2X 2G6Canada Mogile Technologies 16647 Hymus BlvdKirklandQuebec H9H 4R9Canada

The rapid phase-in of electric vehicles(ev)will cause unprecedented issues with managing the supply of electricity and charging *** is in the interest of utility providers and everyday consumers to be able to plan for peak charging times,and related *** past work has been done for localized,short-term forecasting,it has not included longer term forecasting,or considered the relationships between multiple ***,past work has also not offered a framework for dataset construction and evaluated different dataset *** propose a methodology to forecast demand at public ev charging stations,and use it to explore the potential of data-driven models to predict demand up to one week in *** strategy includes selecting parameters for formatting a dataset given a list of charging events,a way to consider flexible prediction horizons,and deployment of deep and supervised learning-based *** the best of our knowledge,ours is the first study to propose machine learning to forecast medium-term public ev charging demand,to exploit weather and other features at public charging stations,and to forecast demand at multiple stations and the entire *** validated our approach using data from eleven stations over three years from Scotland,*** method outperforms the benchmark time series method,and predicts network demand with a symmetric mean absolute percentage error(SMAPE)of 5.9%and a mean absolute error(MAE)of 124.7 kWh,or less than twelve percent of average daily demand.

关键词： public ev charging Forecasting Energy consumption Smart grid Time-series Multi-task learning Deep learning

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