  In this study we investigate the heat load patterns in one building using
multi-step forecasting model. We combine the Autoregressive models that use
multiple eXogenous variables (ARX) with Seasonally adaptable Time of Week and
Climate dependent models (S-TOW-C) (to correct model inaccuracies), to obtain a
robust and accurate regression model that we called S-TOW-C-ARX used in time
series forecasting. Based on the experiment results, it has been shown that the
proposed model is suitable for short term heat load forecasting. The best
forecasting performance is achieved in winter term where the prediction values
are from 4 to 20 % away from the targets, which are commonly seen as very good
values.
