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. Non-linear models designed to capture the sudden and fast-reverting spikes in the price dynamics yield more accurate forecasts. Modeling each season independently also leads to better
results. Finally, pooling forecasts gives more reliable results. Individual models are generally superior
but their performance is more unstable across hours and seasons.
