Journal
IEEE TRANSACTIONS ON POWER SYSTEMS
Volume 31, Issue 2, Pages 994-1005Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2015.2416433
Keywords
Electricity price; factor model; forecasting; principal components; vector autoregression
Categories
Funding
- Ministry of Science and Higher Education (MNiSW, Poland)
- Croatian Science Foundation [IP-2013-11-2203]
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In this paper we investigate whether considering the fine structure of half-hourly electricity prices, the market closing prices of fundamentals (natural gas, coal and) and the system-wide demand can lead to significantly more accurate short-and mid-term forecasts of APX U.K. baseload prices. We evaluate the predictive accuracy of a number of univariate and multivariate time series models over a three-year out-of-sample forecasting period and compare it against that of a benchmark autoregressive model. We find that in the short-term, up to a few business days ahead, a disaggregated model which independently predicts the intra-day prices and then takes their average to yield baseload price forecasts is the best performer. However, in the mid-term, factor models which explore the correlation structure of intra-day prices lead to significantly (as measured by the Diebold-Mariano test) better baseload price forecasts. At the same time, we observe that the inclusion of fundamental variables-especially natural gas prices (in the short-term) and coal prices (in the mid-term)-provides significant gains. The prices, on the other hand, generally do not improve the price forecasts at all, at least in the time period considered in this study (April 2009-December 2013).
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