Principal components based robust vector autoregression prediction of Turkey’s electricity consumption

Abstract

A first order vector autoregression topology was used to model and predict Turkey’s net electricity consumption in the future. Input variables for the model were the annual values of electricity consumption along with four demographic and economic indicators such as, population, gross domestic product, imports and exports. Output variables were the one-step-ahead values of the same variables. First, polynomial regressions were used to determine and remove the trend components of all these five variables. Then, principal components regression method was applied to evaluate the coefficients of the vector autoregression model. Electricity consumption of Turkey was modeled using annual data from 1970 to 2016 and the model was used to predict future consumption values until year 2030. Singular value decomposition was used to determine the number of important dimensions in the data. This approach yielded a significant reduction in the dimensionality of the problem and thus provided robustness to the predictions. The results showed the feasibility of applying principal components regression method to vector autoregression model for electricity consumption prediction. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.