Naslov
Neural Network-based Prediction of Fuel Consumption of a Conventional Delivery Vehicle based on GPS-collected Tracking Data

Autori
Hihlik, Mislav ; Topić, Jakov ; Škugor, Branimir ; Deur, Joško

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Skup
15th Conference on Sustainable Development of Energy, Water and Environment Systems (SDEWES 2020)

Mjesto i datum
Köln, Njemačka; online, 01.09.2020. - 05.09.2020

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Nije recenziran

Ključne riječi
Delivery vehicles ; fuel consumption prediction ; deep neural networks ; big data

Sažetak
This paper deals with finding an appropriate data- driven model for a prediction of vehicle fuel consumption on destination. Several models are considered for this purpose, ranging from a simple polynomial-based models which include only vehicle travelled distance and payload mass as inputs to more complex neural network (NN)-based ones. Special effort is devoted to preparation of inputs for considered NNs, since driving cycles for which the fuel consumption is being predicted can vary in length, while NNs assume an input with fixed dimensions. To this end, two input types are defined and analysed: (i) 1D vector which contains counted discrete vehicle velocity values, and (ii) 2D matrix which contains counted combinations of discrete vehicle velocity and acceleration values. The structures of considered NN architectures are optimised to find the optimal number of their hidden layers and corresponding number of neurons. The proposed models are developed and validated based on a dataset collected by using GPS/GPRS equipment on a set of 10 delivery vehicles during 24h per day over three-month period. Finally, the impact of payload mass on the fuel consumption and its contribution to improvement of fuel prediction accuracy is comprehensively analysed.

Izvorni jezik
Engleski

Znanstvena područja
Strojarstvo

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