Neural Network and Time Series Analysis Approaches in Predicting Electricity Consumption of Public Transportation Vehicles

Public transportation is a relevant issue to be considered in urban planning and in network design, thus efficient management of modern electrical transport systems is a very important but difficult task. Tram and trolley-bus transport in Sofia, Bulgaria, is largely developed. It is one of the largest consumers of electricity in the city, which makes the question of electricity prediction very important for its operation. In fact, they are required to notify the energy provider about the expected energy consumption for a given time range. In this paper, two models are presented and compared in terms of predictive performances and error distributions: one is based on Artificial Neural Networks (ANN) and the other on Time Series Analysis (TSA) methods. They will be applied to the energy consumption related to public transportation, observed in Sofia, during 2011, 2012 and 2013. The main conclusion will be that the ANN model is much more precise but requires more preliminary information and computational efforts, while the TSA model, against some errors, shows a low demanding input entries and a lower power of calculation. In addition, the ANN model has a lower time range of prediction, since it needs many recent inputs in order to produce the output. On the contrary, the TSA model prediction, once the model has been calibrated on a certain time range, can be extended at any time period.