US 11,913,795 B2
Computer-implemented method of predicting energy use for a route
Theodoros Kasioumis, Hayes (GB); Hiroya Inakoshi, London (GB); Makiko Hisatomi, Amersham (GB); and Sven Van den Berghe, Marlow Bucks (GB)
Assigned to FUJITSU LIMITED, Kawasaki (JP)
Filed by Fujitsu Limited, Kawasaki (JP)
Filed on Jun. 23, 2021, as Appl. No. 17/355,754.
Claims priority of application No. 20187503 (EP), filed on Jul. 23, 2020.
Prior Publication US 2022/0026228 A1, Jan. 27, 2022
Int. Cl. G01C 21/34 (2006.01); G01C 21/28 (2006.01); G06N 5/02 (2023.01); G06N 3/08 (2023.01); G06N 20/00 (2019.01)
CPC G01C 21/3469 (2013.01) [G01C 21/28 (2013.01); G06N 3/08 (2013.01); G06N 5/02 (2013.01); G06N 20/00 (2019.01)] 15 Claims
OG exemplary drawing
 
1. A computer-implemented method of predicting energy use for a route comprising:
inputting map data of roads included in K trips in a geographical area, and inputting predictors of rate of energy use along the roads;
inputting energy consumption data of the K trips, the energy consumption data indicating total energy use T between a start point A and an end point B of each of the K trips;
dividing each of the roads in the map data for all the K trips into segments of length measure λi;
grouping the segments from the K trips into a number N of clusters, the clusters being defined in accordance with ranges of at least one of the predictors of rate of energy use and each cluster being defined as having a weight Wj which is to be determined;
using an algorithm to build a model predicting the weight Wj based on solving a system of equations, one per trip, each equation equating a known total energy use T of a trip with a sum of a known length measure of each segment in the trip multiplied by a weight for a cluster into which a segment was grouped;
for each segment, assigning the predicted weight Wj applied to the cluster in which the segment was grouped as a predicted rate of energy use Yi; and
storing a segment identifier (ID) with an indication of the predicted rate of energy use Yi to thereby allow determination of a prediction of energy use for the route in the geographical area based on collective processing of one or more segments among the segments grouped into the clusters.