Case Study for Energy Efficiency Measures of Buildings on an Urban Scale

The energy efficiency of existing buildings is one of the challenges launched by the EPBD recast. The RWTH Aachen University accepted this challenge and started the project EnEff: Campus - Roadmap aiming at reducing the specific primary energy consumption of the university campus building stock (about 300 buildings) by 50 % until 2025. For the estimation of refurbishments for this kind of big data, data mining techniques can be used like the CART method (Classification and Regression Tree). In this investigation, the method applied on the RWTH Aachen buildings stock and the estimated results will be compared to results from a simple data mining technique, called visual method. The comparison is performed by using low-order dynamic building model (LOM) performance simulation through the Modelica AixLib. The determined results of the recommendation of the CART method will be discussed and evaluated in this paper.