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Forecasting of daily electricity spot prices by incorporating intra-day relationships: Evidence form the UK power market

Katarzyna Maciejowska () and Rafał Weron ()

No HSC/13/01, HSC Research Reports from Hugo Steinhaus Center, Wroclaw University of Technology

Abstract: We show that incorporating the intra-day relationships of electricity prices improves the accuracy of forecasts of daily electricity spot prices. We use half-hourly data from the UK power market to model the spot prices directly (via ARX and Vector ARX models) and indirectly (via factor models). The forecasting performance of five econometric models is evaluated and compared with that of a univariate model, which uses only (aggregated) daily data. The results indicate that there are forecast improvements from incorporating the disaggregated data, especially, when the forecast horizon exceeds one week. Additional improvements are achieved when the correlation structure of the intra-day relationships is explored.

Keywords: Electricity spot price; Forecasting; Disaggregated data; Vector autoregression; Factor model; Principal components (search for similar items in EconPapers)
JEL-codes: C32 C38 C53 Q47 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ene and nep-for
Date: 2013-02-14, Revised 2013-04-15
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Downloads: (external link) Final published version (IEEE Conference Proceedings, 10th International Conference on the European Energy Market (EEM'13), DOI 10.1109/EEM.2013.6607314) (application/pdf)

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