Electricity consumption due to transportation systems is a very important parameter to be monitored and studied in large cities, in order to optimize the energy management. Additional economic and environmental benefits can be obtained if a proper and reliable description and forecast of energy absorption is available. In this paper, a Time Series Analysis Model is presented and applied to the electricity consumption of public transportation in Sofia (Bulgaria). This method is able to consider the trend, the periodic and the random components of a certain set of data varying over the time, with the aim of forecasting future slope of the data. The strong periodic feature of the dataset will allow to build a good predictive model, thanks to the implementation of multiple seasonality in charge to reconstruct the daily, weekly and monthly periodicities. The triple seasonality model will show better performances with respect to the double seasonality one, in terms of error statistics, distribution and randomness. In addition, a proper interpretation of the model coefficients will open the way to the implementation of improved energy management processes.

Time Series Analysis and Forecast of the Electricity Consumption of Local Transportation

TEPEDINO, CARMINE;GUARNACCIA, CLAUDIO;QUARTIERI, Joseph
2014

Abstract

Electricity consumption due to transportation systems is a very important parameter to be monitored and studied in large cities, in order to optimize the energy management. Additional economic and environmental benefits can be obtained if a proper and reliable description and forecast of energy absorption is available. In this paper, a Time Series Analysis Model is presented and applied to the electricity consumption of public transportation in Sofia (Bulgaria). This method is able to consider the trend, the periodic and the random components of a certain set of data varying over the time, with the aim of forecasting future slope of the data. The strong periodic feature of the dataset will allow to build a good predictive model, thanks to the implementation of multiple seasonality in charge to reconstruct the daily, weekly and monthly periodicities. The triple seasonality model will show better performances with respect to the double seasonality one, in terms of error statistics, distribution and randomness. In addition, a proper interpretation of the model coefficients will open the way to the implementation of improved energy management processes.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4642830
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