Policy makers and energy operator have the responsibility to select indicators for their mission to lead the renewable energy transition ensuring energy independence and security of supply in the context of decarbonisation of the energy mix and and/or nuclear phase-out with increasing cost for flexibility. Engineers are therefore asked to propose key performance indicators (KPI) allowing to quantify the positive impact of operation strategies and efficient technology solutions to harvest and distribute more renewable resources, while minimizing the environmental impact and overall costs. The aim of this paper is to analyze the impact of KPIs and their different definitions on planning building energy systems (BES) in order to support decision maker to define, monitor and fulfill their objective. A wide-range of alternative solutions are generated using Mixed Linear Integer Programming (MILP) and Multi Objective Optimization (MOO) to capture the decision space of BES. Machine learning techniques, like principle component analysis and k-medoids clustering, are applied to identify the major trends, thus supporting multi - criteria decision making. Results highlight the correlations between twenty-six indicators, showing the importance of (i) setting appropriate system boundaries, (ii) using hourly resolution and (iii) constructional footprint to characterize flexible systems. Low emission electrical grid mix has a high impact on strategic planning and is in conflict with decentralized, self-sufficient energy systems. Including life cycle assessment (LCA) of the system shows besides operational emission, the constructional footprint is significantly contributing to the total Global Warming Potential (GWP). Considering the ecological optimal BES in Switzerland, this contribution is more than 40%, while for high emission electrical grid mix the latter accounts for more than 90%.