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Accurate forecasts of electricity spot prices are essential to the dailyoperational and planning decisions made by power producers and distributors.Typically, point forecasts of these quantities suffice, particularly in theNord Pool market where the large quantity of hydro power leads to pricestability. However, when situations become irregular, deviations on the pricescale can often be extreme and difficult to pinpoint precisely, which is aresult of the highly varying marginal costs of generating facilities at theedges of the load curve. In these situations it is useful to supplant a pointforecast of price with a distributional forecast, in particular one whose tailsare adaptive to the current production regime. This work outlines a methodologyfor leveraging published bid/ask information from the Nord Pool market toconstruct such adaptive predictive distributions. Our methodology is anon-standard application of the concept of error-dressing, which couples afeature driven error distribution in volume space with a non-lineartransformation via the published bid/ask curves to obtain highly non-symmetric,adaptive price distributions. Using data from the Nord Pool market, we showthat our method outperforms more standard forms of distributional modeling. Wefurther show how such distributions can be used to render `warning systems'that issue reliable probabilities of prices exceeding various importantthresholds.

Using published bid/ask curves to error dress spot electricity price forecasts