A framework for a 10-day ahead probabilistic forecast based on a deterministic model is proposed. The framework is demonstrated on the system price of the Nord Pool electricity market. The framework consists of a two-component mixture model for the error terms (ET) generated by the deterministic model. The components assume the dynamics of “balanced” or “unbalanced” ET respectively. The label of the ET originates from a classification of prices according to their relative difference for consecutive hours. The balanced ET are modeled by a seemingly unrelated model (SUR). For the unbalanced ET we only outline a model. The SUR generates a 240-dimensional Gaussian distribution for the balanced ET. The resulting probabilistic forecast is evaluated by four point-evaluation methods, the Talagrand diagram and the energy score. The probabilistic forecast outperforms the deterministic model both by the standards of point and probabilistic evaluation. The evaluations were performed at four intervals in 2008 consisting of 20 days each. The Talagrand diagram diagnoses the forecasts as under-dispersed and biased. The energy score finds the optimal length of training period and set of explanatory variables of the SUR model to change with time. The proposed framework demonstrates the possibility of constructing a probabilistic forecast based on a deterministic model and that such forecasts can be evaluated in a probabilistic setting. This shows that the implementation and evaluation of probabilistic forecasts as a scenario generating tools in stochastic optimization are possible.