Forecasting demand on the Ukrainian electricity market using socio-economic variables

Main Article Content

Mykhailo Orest Krutsyak http://orcid.org/0000-0003-4437-3346

Abstract

This paper presents econometric methods and statistical datasets about changing socio-economic factors and their influence on electricity demand (on the example of Ukraine) as the instrument for forecasting electricity demand. For this purpose, based on the results of well-known economists' works in the energy field, we selected linear multiple regression equations as the main method and changes in volumes of gross domestic product, the average price for electricity and the Ukrainian population (adapted to the situation in Ukraine due to the lack of reliable statistical data on part of the territory occupied by the Russian Federation for the period of 2013-2017) as the main factors. In the course of the study, the correlation between the exploring variable factors and the demand for electricity was found to be close (for the period 2000-2017). So, the given variables were used to construct linear regression equations for forecasting electricity demand in Ukraine (for the period up to 2035). As a result, it was possible to obtain the value of demand volumes, comparable to the volumes of demand provided by the profile ministries and agencies of Ukraine.

Article Details

How to Cite
KRUTSYAK, Mykhailo Orest. Forecasting demand on the Ukrainian electricity market using socio-economic variables. Economics, Management and Sustainability, [S.l.], v. 4, n. 1, p. 46-57, apr. 2019. ISSN 2520-6303. Available at: <http://jems.sciview.net/index.php/jems/article/view/73>. Date accessed: 26 may 2019. doi: https://doi.org/10.14254/jems.2019.4-1.5.
Section
Articles

References

Bodger, P. S., & Tay, H. S. (1987). Logistic and energy substitution models for electricity forecasting: a comparison using New Zealand consumption data. Technological Forecasting and Social Change, 31(1), 27-48.
CountyMeters information service. (n.d.). World population. Retrieved from http://countrymeters.info.
Egelioglu, F., Mohamad, A. A., & Guven, H. (2001). Economic variables and electricity consumption in Northern Cyprus. Energy, 26(4), 355-362.
Fung, Y. H., & Tummala, V. R. (1993, December). Forecasting of electricity consumption: a comparative analysis of regression and artificial neural network models. In Advances in Power System Control, Operation and Management, 1993. APSCOM-93., 2nd International Conference on (pp. 782-787). IET.
Harris, J. L., & Liu, L. M. (1993). Dynamic structural analysis and forecasting of residential electricity consumption. International Journal of Forecasting, 9(4), 437-455.
International Energy Agency. (n.d.). Statistics. Retrieved from http://www.iea.org/statistics/statisticssearch.
Lakhani, H. G., & Bumb, B. (1978). Forecasting demand for electricity in Maryland: an econometric approach. Technological Forecasting and Social Change, 11(3), 237-259.
Liu, X. Q., Ang, B. W., & Goh, T. N. (1991, November). Forecasting of electricity consumption: a comparison between an econometric model and a neural network model. In Neural Networks, 1991. 1991 IEEE International Joint Conference on (pp. 1254-1259). IEEE.
Makridakis, S., & Wheelwright, S. C. (1989). Forecasting Methods for Managementy for the 21st Century. Simon and Schuster.
Makridakis, S., Wheelwright, S. C., & Hyndman, R. J. (2008). Forecasting methods and applications. John Wiley & Sons.
Ministry of Economic Development and Trade of Ukraine. (2018). Perspectives of the development. Consensus forecast. Retrieved from http://me.gov.ua/Documents/List?lang=uk-UA&tag=Konsensus-prognoz.
Ministry of Energy and Coal Industry of Ukraine. (2017). New Energy strategy of Ukraine until 2035. Retrieved from http://mpe.kmu.gov.ua/minugol/doccatalog/document?id=245239554.
Mohamed, Z., & Bodger, P. (2005). Forecasting electricity consumption in New Zealand using economic and demographic variables. Energy, 30(10), 1833-1843.
National Energy and Utilities Regulatory Commission. (n.d.). Tariffs on Electricity. Retrieved from http://www.nerc.gov.ua/?id=19611.
Rajan, M., Jain, V. K. (1999). Modelling of electrical energy consumption in Delhi. Energy; 24, 351–61.
Sinclair Knight Merz, CAE (Centre for Advanced Engineering, University of Canterbury, NZ) (2000). Electricity Supply and Demand to 2015, 5th ed. Christchurch: CAE University of Canterbury Campus.
State Enterprise National power company UKRENERGO. (n.d.). Dispatch information. Retrieved from https://ua.energy/diyalnist/dyspetcherska-informatsiya.
State Statistics Service of Ukraine (n.d.). Statistics. Retrieved from http://www.ukrstat.gov.ua.
The World Bank. (n.d.). Electric power consumption. Retrieved from https://data.worldbank.org/indicator/EG.USE.ELEC.KH.PC.
Yan, Y. Y. (1998). Climate and residential electricity consumption in Hong Kong. Energy, 23(1), 17-20.
Yau, J. K., Schneeweis, T., Robinson, T. R., & Weiss, L. R. (2007). Chapter 8: Alternative Investments Portfolio Management. CFA Institute Investment Books, 2007(4), 477-578.