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Modelling Electricity Price Expectations in a Day-Ahead Market: A Case of Latvia

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  • Bobinaite Viktorija
  • Zuters Jānis

    (University of Latvia Latvia)

Abstract

The paper aims at modelling the electricity generator’s expectations about price development in the Latvian day-ahead electricity market. Correlation and sensitivity analysis methods are used to identify the key determinants of electricity price expectations. A neural network approach is employed to model electricity price expectations. The research results demonstrate that electricity price expectations depend on the historical electricity prices. The price a day ago is the key determinant of price expectations and the importance of the lagged prices reduces as the time backwards lengthens. Nine models of electricity price expectations are prepared for different natural seasons and types of the day. The forecast accuracy of models varies from high to low, since errors are 7.02 % to 59.23 %. The forecasting power of models for weekends is reduced; therefore, additional determinants of electricity price expectations should be considered in the models and advanced input selection algorithms should be applied in future research. Electricity price expectations affect the generator’s loss through the production decisions, which are made considering the expected (forecasted) prices. The models allow making the production decision at a sufficient level of accuracy.

Suggested Citation

  • Bobinaite Viktorija & Zuters Jānis, 2016. "Modelling Electricity Price Expectations in a Day-Ahead Market: A Case of Latvia," Economics and Business, Sciendo, vol. 29(1), pages 12-26, August.
    • Handle: RePEc:vrs:ecobus:v:29:y:2016:i:1:p:12-26:n:2
    DOI: 10.1515/eb-2016-0017
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    References listed on IDEAS

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    1. Binder, Carola Conces, 2016. "Estimation of historical inflation expectations," Explorations in Economic History, Elsevier, vol. 61(C), pages 1-31.
    2. Schlueter, Stephan, 2010. "A long-term/short-term model for daily electricity prices with dynamic volatility," Energy Economics, Elsevier, vol. 32(5), pages 1074-1081, September.
    3. Panapakidis, Ioannis P. & Dagoumas, Athanasios S., 2016. "Day-ahead electricity price forecasting via the application of artificial neural network based models," Applied Energy, Elsevier, vol. 172(C), pages 132-151.
    4. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    5. Bonacina, Monica & Gulli`, Francesco, 2007. "Electricity pricing under "carbon emissions trading": A dominant firm with competitive fringe model," Energy Policy, Elsevier, vol. 35(8), pages 4200-4220, August.
    6. Marc Nerlove, 1958. "Adaptive Expectations and Cobweb Phenomena," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 72(2), pages 227-240.
    7. J. Scott Armstrong & Kesten C. Green, 2005. "Demand Forecasting: Evidence-based Methods," Monash Econometrics and Business Statistics Working Papers 24/05, Monash University, Department of Econometrics and Business Statistics.
    8. Lyman Mlambo, 2012. "Adaptive And Rational Expectations Hypotheses: Reviewing The Critiques," The International Journal of Economic Behavior - IJEB, Faculty of Business and Administration, University of Bucharest, vol. 2(1), pages 3-15, December.
    9. Johnson, Michael D & Anderson, Eugene W & Fornell, Claes, 1995. "Rational and Adaptive Performance Expectations in a Customer Satisfaction Framework," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 21(4), pages 695-707, March.
    10. Lucas, Robert Jr, 1976. "Econometric policy evaluation: A critique," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 1(1), pages 19-46, January.
    11. Pearce, Douglas K., 1975. "The measurement of price expectations," Journal of Behavioral Economics, Elsevier, vol. 4(1), pages 145-165.
    Full references (including those not matched with items on IDEAS)

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