Economic evaluation of properties based on consumption data
DOI:
https://doi.org/10.34641/clima.2022.186Keywords:
Performance Indicator, Annuity, Evaluation of optimizations, Calculation property evaluationAbstract
The building stock is a major factor for achieving climate targets. By improving existing buildings, their efficiency can be significantly increased, thus reducing emissions. The economic evaluation of consumption data is an essential task for operators of properties in order to identify optimization potential. Here, the costs of heat transfer media, electricity and water are essential. The sole evaluation of building-specific consumption data does not fully allow for cross-building comparisons, since other aspects such as their type of use, size and intensity of use have a significant influence. It is necessary to develop a method that allows this comparison and at the same time can be applied with little effort. This paper presents a method for the economic evaluation of buildings taking into account the type of use, size and intensity of use. The innovative method allows the calculation of annuities for certain consumption categories such as electricity. These are combined into an overall performance indicator (PI) for each building. The scale of the PI is generated dynamically depending on the building data under consideration. Thus, a comparison of different buildings is easily and at the same time individually possible in consideration of the real estate portfolio. The results provide an overview of the potential need for optimization of the building as well as the installed plant technology. The effects of potential optimizations on the economic building performance are calculated based on the annuity method and are also included in the revaluation of the respective building. The method was tested in a study of school buildings in a major city in Germany. The method can be used to compare different combinations of measures and determine the optimal option. As a result, decisions regarding possible building optimization measures can be made transparently and scientifically in the future. This enables a more efficient use of resources.