KIEE International Transactions on Power Engineering

The Korean Institute of Electrical Engineers (대한전기학회)
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Further Advances in Forecasting Day-Ahead Electricity Prices Using Time Series Models

  • Guirguis, Hany S. (Dept. of Economic and Finance, School of Business, Manhattan College) ;
  • Felder, Frank A. (Center of Energy, Economics ＆ Environmental Policy, Edward J. Bloustein School of Planning and Public Policy, Rutgers, The State University of New Jersey)
  • Published : 2004.09.01
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Abstract

Forecasting prices in electricity markets is critical for consumers and producers in planning their operations and managing their price risk. We utilize the generalized autoregressive conditionally heteroskedastic (GARCH) method to forecast the electricity prices in two regions of New York： New York City and Central New York State. We contrast the one-day forecasts of the GARCH against techniques such as dynamic regression, transfer function models, and exponential smoothing. We also examine the effect on our forecasting of omitting some of the extreme values in the electricity prices. We show that accounting for the extreme values and the heteroskedactic variance in the electricity price time-series can significantly improve the accuracy of the forecasting. Additionally, we document the higher volatility in New York City electricity prices. Differences in volatility between regions are important in the pricing of electricity options and for analyzing market performance.

Keywords

References

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