Within the master’s thesis, we investigated the long-term and short-term modelling of the heat response of the building, heated by a heat pump. For the purpose of the research, we prepared several data sets that consisted of weather data, heat pump temperature data and heat response data of the building obtained through the TRNSYS simulation of the reference building. The prepared data was used for learning and cross-validation of models. On the basis of linear and stepwise regression, we built several types of long-term and short-term models for predicting the temperature in the building and compared them with heuristically developed models. Long-term models are regressive and short-term models are autoregressive and autoregressive with exogenous inputs. Due to the used regressors, long-term models are less accurate than short-term ones, but useful for several month predictions, while hort-term ones are more accurate, but useful only for short prediction horizons. The presented long-term models for several month predictions achieve RMSE error of around 0,5 °C, while the most accurate short-term model for a prediction horizon of up to 24 hours predicts the temperature in the building with a RMSE error below 0,05 °C. Presented long-term models are useful for optimizing various heating systems in buildings, and developed short-term models are due to high accuracy useful in model predictive control of heating systems.