ENERGY MAP RECONSTRUCTION BY CROWDSOURCED DATA

Given a computer-implemented method for energy consumption prediction for a vehicle type (120) driven by a driver (125) along a planned trip with detailed context such as date, time, route, traffic, weather, etc, it is an objective of the present invention to increase accuracy of energy consumption predictions. The objective is solved by the method comprising the steps: a) receive data with energy consumption values (330, 340), the data including identifications of drivers (135), types of vehicle (130), and trips (A, B), to which each energy consumption value of the energy consumption values (330, 340) relate, each trip of the trips comprising one or more identifiable road segments (310); b) calculate driver factors, vehicle type factors, and road segments factors, for the identifications of drivers (135), vehicle types (130), and road segments (310) in the data; c) identify at least one driver factor (fd) and vehicle type factor (fv) as corresponding to the driver (120) and the vehicle (125) based on the vehicle type; d) identify one or more planned road segments to complete the planned trip and corresponding road segment factors (fns) for each of the one or more road segments; and e) calculate the energy consumption prediction based on the driver factor (fd), the vehicle type factor (fv), the corresponding road segment factors (fns), and a normalized energy consumption value (vnec) for each of the one or more planned road segments.