Predicting power consumption of shared electric vehicles using regression and cluster analysis
Typ
Examensarbete för masterexamen
Program
Publicerad
2019
Författare
Bergman, Lage
Hildén, Johannes
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
When planing a trip with an electric vehicle, one has to predict how much power
will be consumed in advance in order not to risk running out of power during the
trip. This problem is especially relevant for car-sharing services, as they want to
schedule multiple trips in close succession. Current methods for estimating power
consumption all rely on information regarding the exact route that will be followed,
which in a car-sharing service is not always known beforehand.
This thesis presents prediction models based on historical trip information capable
of predicting power consumption for planned trips with electric vehicles without
information about the specific route. The models are created by combining linear
regression and unsupervised learning, in particular clustering with K-means. Kmeans
is used to cluster users into groups based on previous driving habits. Separate
regression models are then trained for each group in order to make more accurate
predictions. We show that it is possible to predict power consumption using stand
alone regression models and a few simple features such as booking duration and
estimated distance. We also show that these predictions can be improved by the use
of clustering. While these results are satisfactory on their own, we conclude that
improvements can be made to the way distances are estimated in order to further
improve predictions.
Beskrivning
Ämne/nyckelord
Regression , Clustering , Cluster based regression , Electric vehicles , Predictions , Power consumption , Car-sharing