A METHOD FOR PREDICTING THE ANNUAL BUILDING HEATING DEMAND BASED ON LIMITED PERFORMANCE DATA

In this paper, we present an investigation of the possibility to use a neural network combined with a quasi-physical description in order to predict the annual supplied space heating demand (P) for a number of small single family buildings located in the North of Sweden. As a qua si-physical description for P, we used measured diurnal performance da ta from a similar building or simulated data from a steady state energ y simulation software. We show that the required supplied space heatin g demand may be predicted with an average accuracy of 5%. The predicti ons were based on access to measured diurnal data of indoor and outdoo r temperatures and the supplied heating demand from a limited time per iod, ranging from 10 to 35 days. The prediction accuracy was found to be almost independent of what time of the year the measurements were o btained from, except for periods when the supplied heating demand was very small. For models based on measurements from May and for some bui ldings from April and September, the prediction was less accurate. (C) 1998 Elsevier Science S.A. All rights reserved.