7th International Conference on Ambient Systems, Networks and Technologies (ANT) / 6th International Conference on Sustainable Energy Information Technology (SEIT), Madrid, Spain, 23 - 26 May 2016, vol.83, pp.774-781
Even though the number and variety of fuel consumption models projected in the literature are common, studies on their validation using real-life data is not only limited but also does not fit well with the real-time data. In this paper, three statistical models namely Support Vector Machine (SVM), Artificial Neural Network and Multiple Linear Regression are used in term of prediction of total and instant fuel consumption. The models are compared against data collected in real-time from three different passenger vehicles on three routes by causal drive, using a mobile phone application. Our outcomes reveal that, the results obtained by the models vary depending on the total consumption and instant consumption correlation. Support Vector Machine model of fuel consumption expose comparatively better correlation than the other statistical fuel consumption models. (C) 2016 The Authors. Published by Elsevier B.V.