On the invariance of energy influential design parameters in a cold climate – a meta-level sensitivity analysis based on the energy, economy, and building characteristics
Abstract
ABSTRACT
Building performance simulation (BPS) applied at earlier stages of design has the potential to assist design parameter decisions that significantly impact a building’s life cycle. Since at such early stages, numerous design variables are still undetermined (e.g. window type, insulations, etc.) the scenarios to be simulated through BPS will be vast and will require extensive time and computational power. Previous studies have tested the sensitivity of a building’s energy consumption during its operation, to design parameters. Most of those studies, however, have used a single case study in their analysis. Thus, the objective of this paper is to evaluate the dependency of results of such sensitivity analyses, on the case study being used. To accomplish that, a hybrid method combining one-parameter-at-a-time (OAT) and global samplings was used. Within the cold climate scope of Québec, Canada, multiple buildings were used to investigate the sensitivity of energy and economy performance to design parameters (architectural, electrical, and mechanical systems); as well as the sensitivity of parameters’ impact on building models. Results indicate that architectural and electrical parameters are sensitive to the model. To expand on the understanding of the root cause of this sensitive behaviour, hypotheses were developed and evaluated through global sampling.
Read the paper
Related studies
- Chaos and quantum thermalization
- The Dalton quantum chemistry program system
- Cord Blood Leptin and Adiponectin as Predictors of Adiposity in Children at 3 Years of Age: A Prospective Cohort Study
- Quantitative metabolomics by H-NMR and LC-MS/MS confirms altered metabolic pathways in diabetes
- Stable adiabatic quantum batteries
- A novel self-lipid antigen targets human T cells against CD1c(+) leukemias
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.