Modeling and forecasting electricity demand in Azerbaijan using cointegration techniques

Fakhri J. Hasanov*, Lester C. Hunt, Ceyhun I. Mikayilov

*Corresponding author for this work

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    Policymakers in developing and transitional economies require sound models to: (i) understand the drivers of rapidly growing energy consumption and (ii) produce forecasts of future energy demand. This paper attempts to model electricity demand in Azerbaijan and provide future forecast scenarios-as far as we are aware this is the first such attempt for Azerbaijan using a comprehensive modelling framework. Electricity consumption increased and decreased considerably in Azerbaijan from 1995 to 2013 (the period used for the empirical analysis)-it increased on average by about 4% per annum from 1995 to 2006 but decreased by about 412 % per annum from 2006 to 2010 and increased thereafter. It is therefore vital that Azerbaijani planners and policymakers understand what drives electricity demand and be able to forecast how it will grow in order to plan for future power production. However, modeling electricity demand for such a country has many challenges. Azerbaijan is rich in energy resources, consequently GDP is heavily influenced by oil prices; hence, real non-oil GDP is employed as the activity driver in this research (unlike almost all previous aggregate energy demand studies). Moreover, electricity prices are administered rather than market driven. Therefore, different cointegration and error correction techniques are employed to estimate a number of per capita electricity demand models for Azerbaijan, which are used to produce forecast scenarios for up to 2025. The resulting estimated models (in terms of coefficients, etc.) and forecasts of electricity demand for Azerbaijan in 2025 prove to be very similar; with the Business as Usual forecast ranging from about of 1912 to 21 TWh.

    Original languageEnglish
    Article number1045
    Issue number12
    Publication statusPublished - 13 Dec 2016


    • Azerbaijan electricity demand
    • Cointegration and error correction models
    • Forecast scenarios
    • Time series analysis


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