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Energy Systems

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05.09.2018 | Original Paper

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

verfasst von: Kadir Kavaklioglu

Erschienen in: Energy Systems | Ausgabe 4/2019

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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.

Vorheriger Artikel Peak shaving: a planning alternative to reduce investment costs in distribution systems?

Nächster Artikel A procedure to design wide-area damping controllers for power system oscillations considering promising input–output pairs

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Titel: Principal components based robust vector autoregression prediction of Turkey’s electricity consumption
verfasst von: Kadir Kavaklioglu
Publikationsdatum: 05.09.2018
Verlag: Springer Berlin Heidelberg
Erschienen in: Energy Systems / Ausgabe 4/2019
Print ISSN: 1868-3967
Elektronische ISSN: 1868-3975
DOI: https://doi.org/10.1007/s12667-018-0302-z

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