Forecasting wholesale electricity prices: A review of time series models

Rafał Weron

MPRA Paper from University Library of Munich, Germany

Abstract: In this paper we assess the short-term forecasting power of different time series models in the electricity spot market. We calibrate autoregression (AR) models, including specifications with a fundamental (exogenous) variable - system load, to California Power Exchange (CalPX) system spot prices. Then we evaluate their point and interval forecasting performance in relatively calm and extremely volatile periods preceding the market crash in winter 2000/2001. In particular, we test which innovations distributions/processes - Gaussian, GARCH, heavy-tailed (NIG, alpha-stable) or non-parametric - lead to best predictions.

Keywords: Electricity price forecasting; heavy tailed distribution; autoregression model; GARCH model; non-parametric noise; system load (search for similar items in EconPapers)
JEL-codes: C22 C46 C53 Q47 (search for similar items in EconPapers)
Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/21299/1/MPRA_paper_21299.pdf original version (application/pdf)

Related works:
Chapter: Forecasting Wholesale Electricity Prices: A Review of Time Series Models (2009)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:21299

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().