Yeonsook Heo

Uncertainty quantification of microclimate variables in building energy models

The last decade has seen a surge in the need for uncertainty analysis (UA) for building energy assessment. The rigorous determination of uncertainty in model parameters is a vital but often overlooked part of UA. To undertake this, one has to turn ones attention to a thriving area in engineering statistics that focuses on uncertainty quantification (UQ) for short. This paper applies dedicated methods and theories that are emerging in this area of statistics to the field of building energy models, and specifically to the microclimate variables embedded in them. We argue that knowing the uncertainty in these variables is a vital prerequisite for ensuing UA of whole building behaviour. Indeed, significant discrepancies have been observed between the predicted and measured state variables of building microclimates. This paper uses a set of approaches from the growing UQ arsenal, mostly regression-based methods, to develop statistical models that quantify the uncertainties in the following most significant microclimate variables: local temperature, wind speed, wind pressure and solar irradiation. These are the microclimate variables used by building energy models to define boundary conditions that encapsulate the interaction of the building with the surrounding physical environment. Although our analysis is generically applicable to any of the current energy models, we will base our UQ examples on the energy model used in EnergyPlus.

Evaluation of calibration efficacy under different levels of uncertainty

This paper examines how calibration performs under different levels of uncertainty in model input data. It specifically assesses the efficacy of Bayesian calibration to enhance the reliability of EnergyPlus model predictions. A Bayesian approach can be used to update uncertain values of parameters, given measured energy-use data, and to quantify the associated uncertainty. We assess the efficacy of Bayesian calibration under a controlled virtual-reality setup, which enables rigorous validation of the accuracy of calibration results in terms of both calibrated parameter values and model predictions. Case studies demonstrate the performance of Bayesian calibration of base models developed from audit data with differing levels of detail in building design, usage, and operation.

Quantitative risk management for energy retrofit projects

This article presents a risk analysis method based on Bayesian calibration of building energy models. The Bayesian approach enables probabilistic outputs from the energy model, which are used to quantify risks associated with investing in energy conservation measures in existing buildings. This article demonstrates the applicability of the proposed methodology to support energy saving contracts in the context of the energy service company industry. A case study illustrates the importance of quantifying relative risks by comparing the probabilistic outputs derived from the Bayesian approach with standard practices endorsed by International Performance Measurement and Verification Protocol and ASHRAE guideline 14.

Influence of error terms in Bayesian calibration of energy system models

Calibration represents a crucial step in the modelling process to obtain accurate simulation results and quantify uncertainties. We scrutinize the statistical Kennedy & O’Hagan framework, which quantifies different sources of uncertainty in the calibration process, including both model inputs and errors in the model. In specific, we evaluate the influence of error terms on the posterior predictions of calibrated model inputs. We do so by using a simulation model of a heat pump in cooling mode. While posterior values of many parameters concur with the expectations, some parameters appear not to be inferable. This is particularly true for parameters associated with model discrepancy, for which prior knowledge is typically scarce. We reveal the importance of assessing the identifiability of parameters by exploring the dependency of posteriors on the assigned prior knowledge. Analyses with random datasets show that results are overall consistent, which confirms the applicability and reliability of the framework.

STOCHASTIC ENERGY SIMULATION FOR RISK ANALYSIS OF ENERGY RETROFITS

Building energy modeling is a common procedure for the analysis of energy efficiency retrofits. Smaller retrofits of isolated systems, such as equipment motors and lighting systems, can often be made without the need for complete energy modeling; however, when the retrofit affects multiple systems, such as those involving the building envelope or the heating or cooling system, or when the retrofits of motors and lighting systems are so significant that they affect the heating and cooling load of the building, a more complete energy analysis is necessary. Because the exact inputs to building energy models are never known, and some inputs to the model are stochastic in nature (e.g., occupancy, plug-loads, lighting loads, weather), deterministic prediction of energy use is not only invariably inaccurate, it is actually inappropriate. When simple deterministic energy savings without uncertainty are used in economic analyses (e.g., return on investment), it is difficult to analyze the risk/benefit of the retrofit investment with true accuracy. A stochastic simulation, which includes the effects of input uncertainty and stochastic inputs, is a more appropriate way to predict the building energy use. In this paper, we present a method for stochastic energy simulation that propagates probability characterizations of the input values through a computational engine to create probable energy use predictions. When this probable energy use is combined with forecasts of energy and construction costs, a probable estimate of return on energy efficiency measure investment is generated, and an economic risk/benefit analysis of the investment can be made. Such information is especially important to the growing energy service company market. The computational engine is based on the CEN/ISO monthly building energy calculation standards so its accuracy is well researched and validated, and the computational simplicity allows for efficient stochastic analysis.