Load demand prediction for mid or long-term horizons is important for the development of any model for electric power system planning. Literature on this topic is much scarcer than short-term forecasting, mainly due to the inherent difficulties in long-term modelling. The aim of this paper is to develop a general multi-rate methodology in order to forecast optimally load demand series sampled at an hourly rate for a mid-term horizon. This method may be considered as an extension of a previously published short-term approach to predict load and prices based on unobserved components. This approach implies the estimation of different models for the same data sampled at different rates (monthly and hourly in this paper). Each model incorporates the appropriate features of the data for its respective sampling interval, and both types of forecasts are integrated in one single forecast by efficient time aggregation techniques that result natural to implement in a State Space framework. The procedure is evaluated by a thorough forecasting experiment in which 365 rolling sets of one hour up to 12 weeks ahead of hourly forecasts are produced for the load demand registered at a transformer of a UK company. The results show that this method produces a notable reduction on the prediction error and its variability. (C) 2009 Elsevier Ltd. All rights reserved.