Next Article in Journal
Comparison of Two Processes Forming CaCO3 Precipitates by Electrolysis
Next Article in Special Issue
Deep Neural Network Based Demand Side Short Term Load Forecasting
Previous Article in Journal
Validation of Generic Models for Variable Speed Operation Wind Turbines Following the Recent Guidelines Issued by IEC 61400-27
Previous Article in Special Issue
Forecasting the State of Health of Electric Vehicle Batteries to Evaluate the Viability of Car Sharing Practices
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessArticle
Energies 2016, 9(12), 1045; doi:10.3390/en9121045

Modeling and Forecasting Electricity Demand in Azerbaijan Using Cointegration Techniques

1
King Abdullah Petroleum Studies and Research Center (KAPSARC), P.O. Box 88550, Riyadh 11672, Saudi Arabia
2
Institute of Control Systems, Institute of Cybernetics, B. Vahabzade Street 9, Baku AZ1141, Azerbaijan
3
Surrey Energy Economics Centre (SEEC), University of Surrey, Guildford, Surrey GU2 7XH, UK
4
Department of Economics and Administrative Sciences, Qafqaz University, H. Aliyev Street 120, Khirdalan AZ0101, Azerbaijan
5
Institute for Scientific Research on Economic Reforms, 88a, Hasan Bey Zardabi Avenue, Baku AZ1011, Azerbaijan
*
Author to whom correspondence should be addressed.
Academic Editor: Francisco Martínez-Álvarez
Received: 31 July 2016 / Revised: 15 November 2016 / Accepted: 21 November 2016 / Published: 13 December 2016
(This article belongs to the Special Issue Energy Time Series Forecasting)
View Full-Text   |   Download PDF [1192 KB, uploaded 21 December 2016]   |  

Abstract

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 4½% 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 19½ to 21 TWh. View Full-Text
Keywords: Azerbaijan electricity demand; time series analysis; cointegration and error correction models; forecast scenarios Azerbaijan electricity demand; time series analysis; cointegration and error correction models; forecast scenarios
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Hasanov, F.J.; Hunt, L.C.; Mikayilov, C.I. Modeling and Forecasting Electricity Demand in Azerbaijan Using Cointegration Techniques. Energies 2016, 9, 1045.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top