The project has developed methods for obtaining and using data from energy certificates (EPCs). In four pilots EPCs have been used as input data to a modelled optimisation of heating and electricity use in buildings, by use of multiple prediction modelling (MPC). The goal of the MPC design has been to optimise greenhouse gas emission savings. The four pilot cases are related to different infrastructure, namely: (1) an office building in Seville, (2) a university building in Salzburg, (3) a residential building in Vienna, and (4) an apartment complex in Berlin. Two cases have investigated the possibility to optimise in relation to use of renewable energy through electricity from grid, and the use of local production and storage of renewable energy when the grid produces fossil energy. The other two cases concern among other things optimisation of heating through improved integration between local heating based on renewables and district heating. The cases have aimed to implement smart control based on data on weather conditions and ambient/outdoor temperature. A key barrier for further implementation of the cases is access to EPC data and that EPC data quality shows variations between countries and regions. The evaluation shows that design of the optimisation algorithm for the MPC is a complex, but ultimately solvable task. The algorithm must make a decision that concerns the balance between different environmental impact categories. Care should be taken to alleviate future lock-in of decisions, and to minimise any potential for runaway use of internet servers. Higher greenhouse gas savings are associated with no increase in comfort level and that the solutions substitute fossil energy. Less savings are associated with cases in which district heating and grid electricity are already renewable, and where users increase comfort level. Large-scale implementation of these local solutions for heating and electricity use can under the right circumstances contribute to faster implentation of renewable energy both for heating and electricity, and can reduce the need for centralised solutions for energy production.