Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models

Rafał Weron and Adam Misiorek

MPRA Paper from University Library of Munich, Germany

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, 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, while this is not necessarily the case when air temperature is considered as the exogenous variable, and that (ii) semiparametric models generally lead to better point and interval forecasts than their competitors, more importantly, they have the potential to perform well under diverse market conditions.

Keywords: Electricity market; Price forecast; Autoregressive model; Nonparametric maximum likelihood; Interval forecast; Conditional coverage (search for similar items in EconPapers)
JEL-codes: C22 C53 Q40 (search for similar items in EconPapers)
Date: 2008-06-10
New Economics Papers: this item is included in nep-ene, nep-for and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (179)

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Journal Article: Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models (2008)
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