Analysis of uncertainty in building design evaluations and its implications

Uncertainties in energy consumption introduced by building operations and weather for a medium-size office building

“…However, significant deviations in terms of building energy consumption between measured performance and modelpredicted results at design stage are reported for low-energy buildings [1]. Deviations between predicted and actual building energy consumption can be attributed to uncertainties introduced by four components of such projections: (1) the accuracy of the 4 underlying models in simulation tools, (2) the accuracy of input parameters describing the design conditions of building envelopes and HVAC systems, (3) actual weather, (4) actual building operations. An estimate of the degree of uncertainties contributed from each factor is of importance to improve the robustness of simulation models and help the modeler and customer have a better understanding of building simulation results.…”

“…Holm and Kuenzel [3] evaluated the impacts of materials properties and surface coefficients on hygrothermal building simulation using a Monte Carlo analysis. De Wit and Augenbroe [4] addressed the effects of uncertainty in two important factors -wind-pressure coefficients and room airtemperature distribution-on simulation results for design evaluation. Macdonald and Clarke [5] integrated uncertainty algorithms within the engine of ESP-r simulation tool.…”

Bayesian calibration of building energy models with large datasets

“…The method of Morris belongs to the class of One-factor-At-a-Time (OAT) design (only one parameter changes values between consecutive simulations) and is suitable when the number of input factors are so large that other variance-based approaches are computationally prohibitive (Saltelli et al, 2008). Therefore, it is a common technique for carrying out sensitivity analysis in building energy models (Heo et al, 2012;De Wit and Augenbroe, 2002;Tian, 2013;Menberg et al, 2016;Kristensen and Petersen, 2016). The main advantage of the Morris method is its relatively lower computation cost as compared to other global sensitivity analysis methods, making it particularly well-suited for use with building energy models where the number of uncertain parameters is high.…”

“…According to (De Wit and Augenbroe, 2002) uncertainty in building energy models can be classified as:…”

Review on stochastic modeling methods for building stock energy prediction

“…It uses deterministic simulation models to perform stochastic analysis. It performs an uncertainty analysis or sensitivity analysis through the Monte Carlo sampling method (Lomas and Eppel 1992;Macdonald 2002;de Wit and Augenbroe 2002;Reddy et al 2007;Hopfe 2009;Corrado and Mechri 2009;Eisenhower et al 2012a). In this approach, the values of input parameters are sampled randomly from a given range and then feed into the energy model.…”