Abstract
Recent literature on building energy performance simulation leans toward implementing uncertainty analysis (UA), instead of deterministic solutions, to handle ever-existing and pivotal uncertainties in building design decision-making process. Variations in weather temperature, degradation of building envelope material properties over time, and random behavior of occupants, among all, are the key sources of uncertainty in building energy consumption predictions. The UA couples to the sensitivity analysis (SA) to identify the most influential inputs on the uncertainties of the building energy consumption. This paper describes a newly-developed UA and SA predictive tool for building energy performance simulations. Energy performance simulations are based on a resistance-capacitance thermal model for the building.
For a hypothetical residential building in College Station, Texas, USA, the present work describes and compares predicted probability distribution and sensitivity indexes produced by the UA-SA tool using a transient (dynamic) response analysis (TRA) and static response analysis (SRA). For brevity, the analysis considers uncertainty only for the exterior walls’ parameters including thickness, thermal conductivity, heat transfer coefficient, density, and heat capacity; i.e., a five-dimensional problem is solved. Compared to the TRA, predictions from the SRA underestimate the annual energy consumption up to 30%; however, SRA is significantly faster. Nonetheless, sensitivity indexes from the SRA and TRA closely match.
Simulation-based analysis of optimized PCM to improve building energy performance and indoor thermal environment
