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Large discrepancies can occur between building energy performance simulation (BEPS) outputs and reference data. Uncertainty and sensitivity analyses are performed to discover the significant contributions of each input parameter to these discrepancies. Variance-based sensitivity analyses typically require many stochastic simulations, which is computationally demanding (especially in the case of the large number of input parameters involved in the analysis). To overcome these impediments, this study proposes a reliable meta-model-based sensitivity analysis, including validation, Morris' method, multivariate adaptive regression splines (MARS) meta-modeling, and Sobol' method, to identify the most influential input parameters on BEPS prediction (annual energy consumption) at the early building design process. A hypothetical building is used to analyze the proposed methodology. Six statistical metrics are applied to verify and quantify the accuracy of the model. It is concluded that the cooling set-point temperature and g-value of the window are the most influential input parameters for the analyzed case study. [ABSTRACT FROM AUTHOR]

Climate neutrality goals in the building sector require a large-scale estimation of environmental impacts for various stakeholders. Life Cycle Assessment (LCA) is a viable method for this purpose. However, its high granularity, and subsequent data requirements and effort, hinder its propagation, and potential employment of Machine Learning (ML) applications on a larger scale. The presented paper outlines the current state of research and practice on district-scale building LCA in terms of standards, software and certifications, and data availability. For this matter, the authors present the development and application of two district-scale LCA tools, Teco and DisteLCA, to determine the Global Warming Potential (GWP) of three different residential districts. Both tools employ data based on (including, but not limited to) CityGML, TABULA, and ÖKOBAUDAT. The results indicate that DisteLCA's granular approach leads to an overestimation of environmental impacts, which can be derived from the statistical approach to operational energy use and related emissions. While both tools lead to substantial time savings, Teco requires less manual effort. The linkage of the aforementioned data sources has proven laborious and could be alleviated with a common data framework. Furthermore, large-scale data analysis could substantially increase the viability of the presented approach. [ABSTRACT FROM AUTHOR]

Heating and ventilation in buildings and vehicles are responsible for over a third of global final energy usage and the resulting emissions. Local climatization can help to save energy while at the same time enable more individualized and adapted micro-climates around people. For the domain of local comfort, integrative test environments for sensors, actuators, and control software are scarce, and oftentimes tailored to a specific set of components. Here, a server-based modular testing infrastructure which allows integration and evaluation of sensors, actuators, and control strategies is presented. Currently, the system is able to integrate, monitor, and log data of thermal imaging, motion sensing, environmental sensor such as temperature or air velocity and to forward signals to actuators such as fans, infrared- or contact-heaters. The generation of control signals is model-based and relies on user feedback provided via the system's smartphone app. Lastly, learning algorithms can be trained and compared during user studies. Practical Application: Although developed in a laboratory based research context, the proposed system is based on open standards and protocols. It therefore can be applied by practitioners, developers, and manufacturers in order to test stand-alone components as well as ensembles of sensors and actuators for personalized climatization in an integrative and replicable manner. [ABSTRACT FROM AUTHOR]

Climate change, increasing emissions and rising global temperatures have gradually affected the way we think about the future of our planet. Urban areas possess significant potential for reducing the energy consumption of the overall energy system. In recent years, there is an increasing number of research initiatives related to Urban Building Energy Modelling (UBEM) that focus on simulation processes and validation techniques. Although input data are crucial for the modelling process as well as for the validity of the results, the availability of input data and associated data formats were not analysed in detail. This paper closes the identified knowledge gap by presenting a taxonomic analysis of key UBEM components including: input data formats, simulation tools, simulation results and validation techniques. This paper concludes that over ∼ 95 % of the studies analysed were not reproducible due to the absence of information relating to key aspects of the respective methodologies such as data sources and simulation workflows. This paper also qualifies how weak levels of interoperability, with respect to input and output data, is present in all phases of UBEM. • Taxonomy based review article for Urban Building Energy Modelling and Simulation. • CityGML data models and EnergyPlus simulation tool are prominently used in UBEM. • At all stages in the simulation process, data interoperability is a critical issue. • Large amount of research lacks key information and is not reproducible. [ABSTRACT FROM AUTHOR]

Virtual simulations and calculations are a key technology for future development methods. A variety of tools and methods for calculating thermal comfort have not gained sufficient acceptance in practice due to their inherent complexity. This article investigates alternative means of determining thermal comfort, namely, the linearization of the equivalent temperature calculation. This enables a wide range of users to evaluate thermal comfort in a fast and easy manner, for example, for energy efficiency simulation. A flow and thermal model were created according to the requirements of DIN EN ISO 14505 to determine heat transfer coefficients under calibration conditions. The model to simulate the equivalent temperature in calibration conditions comprises a geometrically realistic 3D model of a human test person according to the standard. The influence of the turbulence model, as well as the influence of the equivalent temperature on the heat transfer coefficient in calibration conditions, was investigated. It was found that the dependence of the equivalent temperature is mandatory. The dependence between the heat transfer and the equivalent temperature was taken into account with a continuous linearization approach. An equation-based implementation methodology is proposed, enabling a quick implementation of comfort evaluation in future simulation models. Two test cases show the capabilities of the new model and its application in future work. [ABSTRACT FROM AUTHOR]

• Urban scale energy simulation using open CityGML datasets and Modelica. • Open datasets lack essential building attributes. • Crucial information such as years of construction, refurbishment states and building functions are not openly available. • Archetype based approach for urban energy simulations require additional information. • Simulation of five hundred open 3D building models. In this paper, simulations of 500 open-source CityGML building models for heating demand calculation are presented. Using an archetype-based approach and statistical enrichment, Modelica simulations are carried out using the TEASER tool and Dymola. The authors compare the simulation results against measurement data and investigate the impact of years of construction, building usages and refurbishment states in the context of urban building energy simulations. The individual buildings are assigned with information over the construction years and building functions using the measurement data. The years of construction and building usages are primarily obtained in the context of two other research projects and are used for the archetype assignments of the individual building models. Hence, these are well-informed assumptions by the authors, resp. the industry partner experts. This paper includes an in-depth investigation of individual building archetypes with respect to the years of construction. Firstly, the buildings are simulated using externally acquired construction years and pre-assigned CityGML building functions. The simulation results are compared against the measured primary heating demands. Due to high discrepancies between simulations and measured data, the authors secondly, simulate the building models with different refurbishment states. In this paper, the authors conclude that open datasets are indeed important, however, for energy performance simulations, attributes such as construction years, building usages and refurbishment states are necessary and should be included in the open datasets to present an added value. [ABSTRACT FROM AUTHOR]