Title Probabilistic deep learning for electric-vehicle energy-use prediction /
Authors Petkevičius, Linas ; Šaltenis, Simonas ; Čivilis, Alminas ; Torp, Kristian
DOI 10.1145/3469830.3470915
eISBN 9781450384254
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Is Part of SSTD '21: 17th international symposium on spatial and temporal databases: virtual, USA, August 23 - 25, 2021.. New York : Association for Computing Machinery, 2021. p. 85-95.. eISBN 9781450384254
Keywords [eng] spatio-temporal data ; e-vehicle energy consumption ; deep neural network ; probabilistic model ; sequential data
Abstract [eng] The continued spread of electric vehicles raises new challenges for the supporting digital infrastructure. For example, long-distance route planning for such vehicles relies on the prediction of both the expected travel time as well as energy use. We envision a two-tier architecture to produce such predictions. First, a routing and travel-time-prediction subsystem generates a suggested route and predicts how the speed will vary along the route. Next, the expected energy use is predicted from the speed profile and other contextual characteristics, such as weather information and slope. To this end, the paper proposes deep-learning models that are built from EV tracking data. First, as the speed profile of a route is one of the main predictors for energy use, different simple ways to build speed profiles are explored. Next, eight different deep-learning models for energy-use prediction are proposed. Four of the models are probabilistic in that they predict not a single-point estimate but parameters of a probability distribution of energy use on the route. This is particularly relevant when predicting EV energy use, which is highly sensitive to many input characteristics and, thus, can hardly be predicted precisely. Extensive experiments with two real-world EV tracking datasets validate the proposed methods. The code for this research has been made available on GitHub.
Published New York : Association for Computing Machinery, 2021
Type Conference paper
Language English
Publication date 2021