We consider a variant of the sequential prediction of arbitrary sequences based on experts advice, called prediction with specialized experts. We design aggregation rules, that sequentially combine the forecasts provided by the experts; the latter are specialized and need not output a prediction at all time instances while the aggregation rules have to. We provide first a review of the literature on specialized experts and take a new look at some aggregation rules (obtained as adaptations or extensions of earlier rules). We then consider an application to the sequential short-term (one-day-ahead) forecasting of electricity consumption; to do so, we consider two data sets, a Slovakian one and a French one, respectively concerned with hourly and half-hourly predictions. We introduce and develop a general methodology to perform the stated empirical studies. The introduced aggregation rules demonstrate an improved accuracy on the data sets at hand; the improvements lie in a reduced mean squared error but also in a more robust behavior with respect to large occasional errors.