A methodology for performance robustness assessment of low-energy

Rajesh Kotireddy, Pieter-Jan Hoes, Jan L.M. Hensen (Eindhoven University of Technology)

CHALLENGE - Uncertainties in building operation and external factors such as occupant behavior, climate change, policy changes etc. impact building performance, resulting in possible performance deviation during operation compared to the predicted performance in the design phase. The probability of occurrences of these uncertainties are usually unknown and hence, scenarios are essential to assess the performance robustness of buildings. 

SOLUTION - Therefore, a non-probabilistic robustness assessment methodology, based on scenario analysis, is developed to identify robust designs. The proposed methodology is described below:

  • Identify decision makers and based on decision maker’s preferences define the following: Building design space, future scenarios, performance and robustness indicators
  • The performance of the design space is predicted for future scenarios by using a building performance simulation model. A detailed building and energy systems model used to predict thermal and energy performance of various designs is developed in TRNSYS. The building and energy systems TRNSYS models are coupled with modeFRONTIER to carry out performance assessment of the design space for the formulated scenarios.
  • Multi-criteria performance assessment: Carry out performance assessment considering multiple performance indicators and corresponding robustness evaluated using a robustness assessment method.
  • Multi-Criteria Decision Making (MCDM): Select robust designs for decision makers by prioritizing the performance indicators based on decision maker preferences.

BENEFITS - Using the MCDM method, a decision maker can easily select a cost optimal robust design from the design space based on design score (Fig. 10) or can trade off the selected designs based on the design score with required additional investment cost. This method is useful for quicker identification of robust designs form a large design space.