Buildings are currently controlled in a sub optimal way, using a WC controller that is dependent only on the external temperature. A rich amount of real-time data from installed sensors is available within the buildings and the network and can be used to counteract this. To better control the indoor temperature and the heat supply this degree-project develops a model and optimizer for control of the indoor temperature, where industry standard data streams are used as inputs. The model and optimizer can be implemented in a MPC which takes the future external temperature into consideration and enhances the ability to control the heat supply. There are two main reasons why enhanced control is interesting to look at, the economic aspects and the comfort of the occupancies. This degree project is focused on developing a general building model for the purpose of utilizing the building as an energy storage for peak-shaving.  The finalized model is a dynamic grey-box model developed using data from a multifamily building, Building A, located in Västerås Sweden. The training period is set to 408 hours, and the prediction horizon is set to 48 hours as a result of the verification. To demonstrate the utilization possibilities of using the building as a heat storage, an optimizer is constructed to evaluate a peak shaving control strategy. The control objective (Qsupply) is controlled by manipulating the indoor temperature (Tin) within a set interval. By setting a fixed interval for the indoor temperature within the comfort interval, the comfort is still maintained. For the peak shaving different flexibilities within the indoor temperature have been examined with a range from 22 +/- 0.25 degrees Celcius to 22 +/- 2.00 degrees Celcius.  The model is verified in 4 steps: prediction ability on the historic data, parametric verification on the time constant, simulation of heat supply separately from the historic data and model generality by implementing the model on a second multifamily building, Building B. The model has a RRMSE of 8% for Building A and 9% for Building B which is considered excellent.  Due to the lack of access to the real building, the developed model is not validated. Based on peak shaving and energy consumption, the preferred solution is 22+/- 1.25 degrees Celcius. But based on surveys about occupancies attitude toward flexibility in the indoor temperature and economical aspects, an indoor temperature of 22 +/- 0.50 degrees Celcius is considered the best choice with the maximum peak in the heat supplied decreased by 35% and the energy consumption is decreased by 10% compared to the historical case. We suggest allowing the customers to choose their preferred flexibility to ensure comfort.