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Modeling and forecasting electricity loads: A comparison

Author

Listed:
  • Rafal Weron

    (Hugo Steinhaus Center)

  • Adam Misiorek

    (Institute of Power Systems Automation)

Abstract

In this paper we study two statistical approaches to load forecasting. Both of them model electricity load as a sum of two components – a deterministic (representing seasonalities) and a stochastic (representing noise). They differ in the choice of the seasonality reduction method. Model A utilizes differencing, while Model B uses a recently developed seasonal volatility technique. In both models the stochastic component is described by an ARMA time series. Models are tested on a time series of system-wide loads from the California power market and compared with the official forecast of the California System Operator (CAISO).

Suggested Citation

  • Rafal Weron & Adam Misiorek, 2005. "Modeling and forecasting electricity loads: A comparison," Econometrics 0502004, EconWPA.
  • Handle: RePEc:wpa:wuwpem:0502004
    Note: Type of Document - pdf; pages: 8. ”The European Electricity Market EEM-04”, Proceedings Volume, pp. 135-142
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    File URL: https://econwpa.ub.uni-muenchen.de/econ-wp/em/papers/0502/0502004.pdf
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    References listed on IDEAS

    as
    1. Smith, Michael, 2000. "Modeling and Short-term Forecasting of New South Wales Electricity System Load," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(4), pages 465-478, October.
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    Citations

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    Cited by:

    1. Janczura, Joanna & Weron, Rafal, 2009. "Regime-switching models for electricity spot prices: Introducing heteroskedastic base regime dynamics and shifted spike distributions," MPRA Paper 18784, University Library of Munich, Germany.
    2. Rafal Weron & Adam Misiorek, 2005. "Forecasting Spot Electricity Prices With Time Series Models," Econometrics 0504001, EconWPA.
    3. Liebl, Dominik, 2010. "Modeling hourly Electricity Spot Market Prices as non stationary functional times series," MPRA Paper 25017, University Library of Munich, Germany.
    4. Szymon Borak & Adam Misiorek & Rafał Weron, 2010. "Models for Heavy-tailed Asset Returns," SFB 649 Discussion Papers SFB649DP2010-049, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    5. Mauritzen, Johannes, 2010. "What happens when it's Windy in Denmark? An Empirical Analysis of Wind Power on Price Volatility in the Nordic Electricity Market," Discussion Papers 2010/18, Norwegian School of Economics, Department of Business and Management Science.
    6. Joanna Janczura & Rafal Weron, 2011. "Black swans or dragon kings? A simple test for deviations from the power law," HSC Research Reports HSC/11/01, Hugo Steinhaus Center, Wroclaw University of Technology.
    7. Janczura, Joanna & Weron, Rafal, 2010. "Goodness-of-fit testing for regime-switching models," MPRA Paper 22871, University Library of Munich, Germany.
    8. Niematallah Elamin & Mototsugu Fukushige, 2017. "Modeling and Forecasting Hourly Electricity Demand by SARIMAX with Interactions," Discussion Papers in Economics and Business 17-28, Osaka University, Graduate School of Economics and Osaka School of International Public Policy (OSIPP).
    9. Montero, José M. & García-Centeno, Maria C. & Fernández-Avilés, Gema, 2011. "Modelling the Volatility of the Spanish Wholesale Electricity Spot Market. Asymmetric GARCH Models vs. Threshold ARSV model/Modelización de la volatilidad en el mercado eléctrico español. Modelos GARC," Estudios de Economía Aplicada, Estudios de Economía Aplicada, vol. 29, pages 597-616, Agosto.
    10. Vazquez, Miguel & Barquín, Julián, 2009. "A fundamental power price model with oligopolistic competition representation," MPRA Paper 15629, University Library of Munich, Germany.
    11. Janczura, Joanna & Weron, Rafal, 2010. "An empirical comparison of alternate regime-switching models for electricity spot prices," Energy Economics, Elsevier, vol. 32(5), pages 1059-1073, September.
    12. Weron, Rafal, 2008. "Bezpieczeństwo elektroenergetyczne: Ryzyko > Zarządzanie ryzykiem > Bezpieczeństwo
      [Power security: Risk > Risk management > Security]
      ," MPRA Paper 18786, University Library of Munich, Germany, revised 2008.
    13. Joanna Janczura & Rafał Weron, 2012. "Efficient estimation of Markov regime-switching models: An application to electricity spot prices," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(3), pages 385-407, July.

    More about this item

    Keywords

    Electricity; load forecasting; ARMA model; seasonal component;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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