Quantification of model form uncertainty in the calculation of solar diffuse irradiation on inclined surfaces for building energy simulation

Traditional uncertainty quantification (UQ) in the prediction of building energy consumption has been limited to the propagation of uncertainties in model input parameters. Models by definition ignore, at least to some degree, and, in almost all cases, simplify the physical processes that govern the reality of interest, thereby introducing additional uncertainty in model predictions that cannot be captured as input parameter uncertainty. Quantification of this type of uncertainty (which we will refer to as model form uncertainty) is a necessary step towards the complete UQ of model predictions. This paper introduces a general framework for model form UQ and shows its application to the widely used sky irradiation model developed by Perez et al. [1990. “Modeling Daylight Availability and Irradiance Components from Direct and Global Irradiance.” Solar Energy 44 (5): 271–289], which computes solar diffuse irradiation on inclined surfaces. We collected a data set of one-year measurements of solar irradiation at one location in the USA. The measurements were done at surfaces with different tilt angles and orientations, for a wide spectrum of sky conditions. A statistical analysis using both this data set and published studies worldwide suggests that the Perez model performs non-uniformly across different locations and produces a certain bias in its predictions. Based on the same data, we then use a two-phase regression model to express model form uncertainty in the use of the Perez model at this particular location. Using a holdout validation test, we demonstrate that the two-phase regression model considerably reduces the model bias errors and root mean square errors for every tilted surface. Lastly, we discuss the significance of including model form uncertainty in the energy consumption predictions obtained with whole building simulation.     

Diagnostic test cases for verifying surface heat transfer algorithms and boundary conditions in building energy simulation programs

Verification and validation are crucial in developing and implementing models. Although there are standards to test energy simulation software, this article describes an additional set of eight test cases that are a combination of analytical cases and numerical cases for solid conduction heat transfer. These tests focus on diagnosing and verifying conductive heat transfer algorithms and boundary conditions in building envelopes or fabrics. As an example, EnergyPlus versions 5, 6 and 7 are tested using these eight test cases. The test cases were useful for detecting several bugs in the code. The authors recommend these test cases as useful complements to existing verification test cases for building envelopes.     

武汉市某政府办公楼的能耗模拟与节能措施分析

根据武汉市某政府办公楼的能源审计与能耗模拟,得到了该大楼的各项能耗指标.给出了围护结构、空调系统、照明系统的节能改造方案以及投资回收期,并探讨了室内温度及系统COP值的设定.     

Overview of pressure coefficient data in building energy simulation and airflow network programs

Wind pressure coefficients (C_p) are influenced by a wide range of parameters, including building geometry, facade detailing, position on the facade, the degree of exposure/sheltering, wind speed and wind direction. As it is practically impossible to take into account the full complexity of pressure coefficient variation, building energy simulation (BES) and Airflow network (AFN) programs generally incorporate it in a simplified way. This paper provides an overview of pressure coefficient data and the extent to which they are currently implemented in BES–AFN programs. A distinction is made between primary sources of C_p data, such as full-scale measurements, reduced-scale measurements in wind tunnels and computational fluid dynamics (CFD) simulations, and secondary sources, such as databases and analytical models. The comparison between data from secondary sources implemented in BES–AFN programs shows that the C_p values are quite different depending on the source adopted. The two influencing parameters for which these differences are most pronounced are the position on the facade and the degree of exposure/sheltering. The comparison of C_p data from different sources for sheltered buildings shows the largest differences, and data from different sources even present different trends. The paper concludes that quantification of the uncertainty related to such data sources is required to guide future improvements in C_p implementation in BES–AFN programs.     

Experimental characterization, modeling and simulation of a wood pellet micro-combined heat and power unit used as a heat source for a residential building

Cogeneration provides heat and power in a more efficient way than separate production. Micro-cogeneration (micro-CHP) is an emerging solution for the improvement of energy and environmental assessments of residential buildings. A wood pellet Stirling engine micro-CHP unit has been studied in order to characterize its annual performance when integrated to a building. First, through a test bench experiment, both transient and steady state behaviors of the micro-CHP unit have been characterized and modeled. Then a more complete model representing a hot water and heating system including the micro-CHP unit and a stratified storage tank has been carried out. This model has been coupled to a building model. A sensitivity analysis by simulation shows that the dimensioning of different elements of the system strongly influences its global energy performance.