A novel ensemble method for electric vehicle power consumption forecasting: Application to the Spanish system
Asencio Cortés, G.
Gastalver Rubio, Adolfo
Troncoso Lora, Alicia
Riquelme Santos, José Cristóbal
Riquelme Santos, Jesús Manuel
|Department||Universidad de Sevilla. Departamento de Ingeniería Electrónica
Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
|Abstract||The use of electric vehicle across the world has become one of the most challenging issues for environmental policies. The galloping climate change and the expected running out of fossil fuels turns the use of such ...
The use of electric vehicle across the world has become one of the most challenging issues for environmental policies. The galloping climate change and the expected running out of fossil fuels turns the use of such non-polluting cars into a priority for most developed countries. However, such a use has led to major concerns to power companies, since they must adapt their generation to a new scenario, in which electric vehicles will dramatically modify the curve of generation. In this paper, a novel approach based on ensemble learning is proposed. In particular, ARIMA, GARCH and PSF algorithms' performances are used to forecast the electric vehicle power consumption in Spain. It is worth noting that the studied time series of consumption is non-stationary and adds difficulties to the forecasting process. Thus, an ensemble is proposed by dynamically weighting all algorithms over time. The proposal presented has been implemented for a real case, in particular, at the Spanish Control Centre for the Electric Vehicle. The performance of the approach is assessed by means of WAPE, showing robust and promising results for this research field.
|Citation||Gómez Quiles, C., Asencio Cortés, G., Gastalver Rubio, A., Martínez-Álvarez, F., Troncoso Lora, A., Manresa, J.,...,Riquelme Santos, J.M. (2019). A novel ensemble method for electric vehicle power consumption forecasting: Application to the Spanish system. IEEE Access, 7 (Article number 2936478), 120840-120856.|