This empirical paper compares the accuracy of 12 time series methods for short-term (day-ahead) spot price forecasting in auction-type electricity markets. The methods considered include standard autoregression (AR) models and their extensions -- spike preprocessed, threshold and semiparametric autoregressions (i.e., AR models with nonparametric innovations) -- as well as mean-reverting jump diffusions. The methods are compared using a time series of hourly spot prices and system-wide loads for California, and a series of hourly spot prices and air temperatures for the Nordic market. We find evidence that (i) models with system load as the exogenous variable generally perform better than pure price models, but that this is not necessarily the case when air temperature is considered as the exogenous variable; and (ii) semiparametric models generally lead to better point and interval forecasts than their competitors, and more importantly, they have the potential to perform well under diverse market conditions.
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Christoffersen, Peter F, 1998.
"Evaluating Interval Forecasts,"
International Economic Review,
Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-62, November.
Huisman, R. & Mahieu, R.J., 2001.
"Regime Jumps in Electricity Prices,"
Research Paper
ERS-2001-48-F&A Revision_, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus Uni.
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