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In this study we investigate the heat load patterns in one building usingmulti-step forecasting model. We combine the Autoregressive models that usemultiple eXogenous variables (ARX) with Seasonally adaptable Time of Week andClimate dependent models (S-TOW-C) (to correct model inaccuracies), to obtain arobust and accurate regression model that we called S-TOW-C-ARX used in timeseries forecasting. Based on the experiment results, it has been shown that theproposed model is suitable for short term heat load forecasting. The bestforecasting performance is achieved in winter term where the prediction valuesare from 4 to 20 % away from the targets, which are commonly seen as very goodvalues.

Data-Driven Model For Heat Load Prediction In Buildings Connected To District Heating Networks