A review of methods to match building energy simulation models to measured data

Abstract

Whole building energy simulation (BES) models play a significant role in the design and optimisation of buildings. Simulation models may be used to compare the cost-effectiveness of energy-conservation measures (ECMs) in the design stage as well as assessing various performance optimisation measures during the operational stage. However, due to the complexity of the built environment and prevalence of large numbers of independent interacting variables, it is difficult to achieve an accurate representation of real-world building operation. Therefore, by reconciling model outputs with measured data, we can achieve more accurate and reliable results. This reconciliation of model outputs with measured data is known as calibration. This paper presents a detailed review of current approaches to model development and calibration, highlighting the importance of uncertainty in the calibration process. This is accompanied by a detailed assessment of the various analytical and mathematical/statistical tools employed by practitioners to date, as well as a discussion on both the problems and the merits of the presented approaches.

Review on stochastic modeling methods for building stock energy prediction

“…A detailed review of the calibration techniques for building simulation can be found elsewhere (Coakley et al 2014;Fabrizio and Monetti 2015). The calibration can be conducted with either deterministic or probabilistic method.…”

Bayesian calibration of building energy models with large datasets

“…According to Coakley et al (2014), calibration approaches for building energy simulation can be broadly defined as either manual or automated.…”

“…Calibration approaches that fall under this category typically requires the energy modeler to perform iterative manual tuning (Reddy, 2006;Coakley et al, 2014) and to have in-depth knowledge of the building and its operation. Therefore, manual calibration approaches usually involve either 1) detailed energy audits to gain a better understanding of the building systems and their operations (Ian Shapiro, 2009;Pedrini et al, 2002;Yoon et al, 2003); 2) intrusive tests where groups of end-use loads are turned on and off in a controlled sequence to provide information on their end-use impact (Soebarto, 1997); 3) collection of high-resolution and high-quality data for empirical validation (Clarke et al, 1993); or 4) a protocol of short-term end-use monitoring to gather data that would help explain the differences between measured and simulated data (Subbarao, 1988;Manke et al, 1996).…”

“…NMBE serves as a good indicator of overall bias in the simulated data, providing an indication as to whether the predicted values tend to overestimate or understimate the actual values (EVO, 2012;ASHRAE, 2002). However, positive bias may be compensated by negative bias resulting in a cancellation of bias (Coakley et al, 2014). Hence, another indicator of model error should be used in conjunction with NMBE.…”

Comparative study of a building energy performance software (KEP-IYTE-ESS) and ANN-based building heat load estimation

“…These tools are widely used for analyzing energy consumption and determining heat load of the buildings [21][22][23][24][25][26]. Boyano et al [25] studied energy demand and potential savings in buildings based on simulation results.…”