Influence of Double Seasonality on Economic Forecasts on the Example of Energy Demand

• Autorzy: Jacek Brożyna , Grzegorz Mentel , Beata Szetela
• Języki publikacji: EN

Abstrakty

EN

The article deals with the issues of forecasting one of the most important elements of modern economies, which is electricity. The size of its production must be properly predicted and it should be technically possible and profitable from the economic point of view. The most frequently used forecasting models in literature are ARIMA and exponential smoothing. However, these models have a major disadvantage as they do not allow forecasting the series with double seasonality and periods of non-integer values. The authors of this article, on the example of data on maximum energy demand, presented an application of one of the latest forecasting models TBATS, which is devoid of this disadvantage. Using this model, the article presents forecasts for the year ahead with information on power consumption for each day, showing that long-term planning without losing details is possible. With such information energy producers are able to optimize production and the economic side of their business overall. (original abstract)

Słowa kluczowe

EN

Electric power; Energy demand; Seasonal character

PL

Energia elektryczna; Zapotrzebowanie na energię; Sezonowość

• Czasopismo: Journal of International Studies
• Rocznik: 2016
• Tom: 9
• Numer: nr 3
• Strony: 9--20

Twórcy

autor

Jacek Brożyna

• Rzeszow University of Technology, Poland

autor

Grzegorz Mentel

• Rzeszow University of Technology, Poland

autor

Beata Szetela

• Rzeszow University of Technology, Poland

Bibliografia

• Balitskiy, S., Bilan, Y., Strielkowski, (2014). W. Energy security and economic growth in the European Union, Journal of Security and Sustainability Issues, 4 (2), pp. 125-132.
• Balitskiy, S., Bilan, Y., Strielkowski, W., Štreimikiene, D. (2016). Energy efficiency and natural gas consumption in the context of economic development in the European Union Renewable and Sustainable Energy Reviews, 55, pp. 156-168. DOI: 10.1016/j.rser.2015.10.053
• Bratu, M. (2012), Accuracy assessement of short run macroeconomic forecasts in Romania. Economics and Sociology, 5 (1), pp. 26-38.
• Bee Dagum, E. & Bianconcini, S., 2016. Seasonal Adjustment Methods and Real Time Trend-Cycle Estimation. Online: Springer.
• Box, G. E. P. & Cox, D. R., 1964. An analysis of transformations. Journal of the Royal Statistical Society, Series B, 26(2), pp. 211-252.
• Box, G. E. P. & Jenkins, G. M., 1976. Time Series Analysis: Forecasting and Control. San Francisco: Holden Day.
• Bratu, M., 2012. A Comparison of Two Quantitative Forecasting Methods for Macroeconomic.
• Brockwell, P. J. & Davis, R. A., 1996. Introduction to Time Series and Forecasting. New York: Springer.
• Brożyna, J., Mentel, G. & Szetela, B., 2016. A Mid-Term Forecast of Maximum Demand for Electricity in Poland. Montenegrin Journal of Economics, 12(2), pp. 73-88.
• Chen, J. F., Wang, W. M. & Huang, C. M., 1995. Analysis of an Adaptive Time-Series Autoregressive Moving-Average (ARMA) Model for Short-Term Load Forecasting. Electric Power Systems Research, Volume 34, pp. 187-196.
• de Andrade, L. C. M. & da Silva, I. N., 2009. Very Short-Term Load Forecasting Based on ARIMA Model and Intelligent Systems. Curitiba, IEEE, pp. 1-6.
• De Livera, A. M., Hyndman, R. J. & Snydera, R. D., 2011. Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing. Journal of the American Statistical Association, 106(496), pp. 1513-1527.
• Gardner, E. S., 1985. Exponential smoothing: The state of the art. Journal of Forecasting, 4(1), pp. 1-28.
• Gardner, J. E. S. & McKenzie, E., 1985. Forecasting trends in time series. Management Science, 31(10), p. 1237-1246.
• Goodhart, C., 2008. The background to the 2007 financial crisis. International Economics and Economic Policy, February, 4(4), p. 331-346.
• Granger, C. W. J., 1980. Forecasting in Business and Economics. San Diego: Academic Press.
• Harvey, A., 1989. Forecasting structural time series models and the Kalman filter. Cambridge Books: Cambridge University Press.
• Holt, C. C., 1957. Forecasting Trends and Seasonals by Exponentially Weighted Averages. Carnegie Institute of Technology: Pittsburgh Office of Naval Research memorandum no. 52.
• Hyndman, R. J. & Koehler, A. B., 2006. Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), pp. 679-688.
• Hyndman, R. J., Koehler, A. B., Ord, J. K. & Snyder, R. D., 2008. Forecasting with Exponential Smoothing: The State Space Approach. New York: Springer.
• Hyndman, R. J., Koehler, A. B., Snyder, R. D. & Grose, S., 2002. A state space framework for automatic forecasting using exponential smoothing methods. International Journal of Forecasting, 18(3), pp. 439-454.
• Lee, C. M. & Ko, C. N., 2011. Short-term load forecasting using lifting scheme and ARIMA models. Expert Systems with Applications, Volume 38, pp. 5902-5911.
• Mentel, G., 2012. Modeling Gas Prices in Poland with an Application of the Vector Autoregression Method (VAR). Folia Oeconomica Stetinensia, vol. 12, issue 2 (Dec), pp. 46-57. DOI: 10.2478/v10031-012-0029-2.
• Nazarczuk, J. M., 2013. Wpływ światowego kryzysu finansowego na gospodarkę Polski i jej regionów. In: R. Kisiel & M. Wojarska, eds. Wybrane aspekty rozwoju regionalnego. Olsztyn: Fundacja "Wspieranie i Promocja Przedsiębiorczości na Warmii i Mazurach", pp. 75-89.
• Nieborak, T., 2010. Globalny kryzys finansowy - istota, przyczyny, konsekwencje. Ruch Prawniczy, Ekonomiczny i Socjologiczny, pp. 95-106.
• Pappas, S. S. et al., 2008. Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models. Energy, September, 33(9), p. 1353-1360.
• Platchkov, L. M. & Pollitt, M. G., 2011. The economics of energy (and electricity) demand. In: T. Jamasb & M. G. Pollitt, eds. The Future of Electricity Demand: Customers, Citizens and Loads. Cambridge: Cambridge University Press, pp. 17-47.
• Snyder, R., 2006. Discussion. International Journal of Forecasting, 22(4), p. 673 - 676.
• Taylor, J. W., 2003. Short-term electricity demand forecasting using double seasonal exponential smoothing. Journal of Operational Research Society, Volume 54, pp. 799-805.
• West, M. & Harrison, J., 1997. Bayesian forecasting and dynamic models. 2nd ed. New York: Springer-Verlag.
• Whittle, P., 1951. Hypothesis Testing in Time Series Analysis. Uppsala: Almquist and Wicksell.
• Zeliaś, A., Pawełek, B. & Wanat, S., 2016. Prognozowanie ekonomiczne: teoria, przykłady, zadania. Warszawa: Wydawnictwo Naukowe PWN.
• Zwolankowski, M., 2011. The Financial Crisis, Financial System Instability and Monetary Transmission Mechanism. Journal of International Studies, 4(1), pp. 26-32.

Identyfikatory

DOI

10.14254/2071-8330.2016/9-3/1