 Forecasting spot electricity prices: A comparison of parametric and semiparametric time series modelsInternational Journal of Forecasting, 2008, vol. 24, issue 4, 744-763 Abstract: 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. Keywords: Electricity; market; Price; forecasts; Autoregressive; model; Nonparametric; maximum; likelihood; Interval; forecasts; Conditional; coverage (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:24:y:2008:i:4:p:744-763 Access Statistics for this article International Journal of Forecasting is currently edited by R. J. Hyndman More articles in International Journal of Forecasting from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.