Forecasting electricity spot prices using time-series models with a double temporal segmentation

Marie Bessec, Julien Fouquau and Sophie Meritet ()

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Abstract: The French wholesale market is set to expand in the next few years under European pressure and national decisions. In this paper, we assess the forecasting ability of several classes of time series models for electricity wholesale spot prices at a day-ahead horizon in France. Electricity spot prices display a strong seasonal pattern, particularly in France given the high share of electric heating in housing during winter time. To deal with this pattern, we implement a double temporal segmentation of the data. For each trading period and season, we use a large number of specifications based on market fundamentals: linear regressions, Markov-switching models, threshold models with a smooth transition. An extensive evaluation on French data shows that modeling each season independently leads to better results. Among non-linear models, MS models designed to capture the sudden and fast-reverting spikes in the price dynamics yield more accurate forecasts. Finally, pooling forecasts gives more reliable results.

Keywords: Electricity spot prices; forecasting; regime-switching (search for similar items in EconPapers)
Date: 2014-03-28
Note: View the original document on HAL open archive server: https://hal.science/hal-01502835v1
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Citations: View citations in EconPapers (13)

Published in 2nd International Symposium on Energy and Finance Issues (ISEFI-2014), Mar 2014, Paris, France. pp.34

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Related works:
Working Paper: Forecasting electricity spot prices using time-series models with a double temporal segmentation (2016)
Working Paper: Forecasting electricity spot prices using time-series models with a double temporal segmentation (2014)
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