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Abstract: Recent results show that a predictive building automation can be used to operate buildings in an energy and cost effective manner with only a small retrofitting requirements. In this approach, the dynamic models are of crucial importance. As industrial experience has shown, modeling is the most time-demanding and costly part of the automation process. Many papers devoted to this topic actually deal with modeling of building subsystems. Although some papers identify a building as a complex system, the provided models are usually simple two-zones models, or extremely detailed models resulting from the use of building simulation software packages. These are, however, not suitable for predictive control. The objective of this paper is to share the years-long experience of the authors in building modeling intended for predictive control of the building''s climate. We provide an overview of identification methods for buildings and analyze their applicability for subsequent predictive control. Moreover, we propose a new methodology to obtain a model suitable for the use in a predictive control framework combining the building energy performance simulation tools and statistical identification. The procedure is based on the so-called co-simulation that has appeared recently as a feature of various building simulation software packages. [Copyright &y& Elsevier]

Occupant behavior is one of the major factors influencing building energy consumption and contributing to uncertainty in building energy use prediction and simulation. Currently the understanding of occupant behavior is insufficient both in building design, operation and retrofit, leading to incorrect simplifications in modeling and analysis. This paper introduced the most recent advances and current obstacles in modeling occupant behavior and quantifying its impact on building energy use. The major themes include advancements in data collection techniques, analytical and modeling methods, and simulation applications which provide insights into behavior energy savings potential and impact. There has been growing research and applications in this field, but significant challenges and opportunities still lie ahead. [ABSTRACT FROM AUTHOR]

Abstract: Building energy performance simulation tools such as EnergyPlus, Ecotect, and eQuest are widely used to model energy performance of existing buildings and assess retrofit alternatives. Nevertheless, predictions from simulations typically deviate from actual measurements. Monitoring actual performance and measuring deviations from simulated data in 3D can help improve simulation accuracy through model calibrations, and in turn facilitate identification of energy performance problem. To do that, this paper presents Energy Performance Augmented Reality (EPAR) modeling that leverages collections of unordered digital and thermal imagery, in addition to computational fluid dynamics (CFD) models. First, users collect large numbers of digital and thermal imagery from the building under inspection using a single thermal camera. Through an image-based reconstruction pipeline, actual 3D spatio-thermal models are automatically generated and are superimposed with expected building energy performance models generated using CFD analysis through a user-driven process. The outcomes are EPAR models which visualize actual and expected models in a common 3D environment. Within the EPAR models, actual measurements and simulated results can be systematically compared and analyzed. The method is validated on typical residential and instructional buildings. The results demonstrate that EPAR models facilitate calibration of building energy performance models and support detection and analysis of building performance deviations. [Copyright &y& Elsevier]

Abstract: Energy consumption of buildings depends significantly on the criteria used for the indoor environment and building design and operation. The environmental factors that define the indoor environmental quality are: thermal comfort, indoor air quality, acoustic comfort and visual comfort. This paper approaches the numerical prediction of thermal comfort in closed spaces on the basis of PMV–PPD model, and its testing to asymmetric or nonuniform thermal radiation, as well as the indoor air quality simulation and control. The following are developed: a computation and testing model of thermal comfort in buildings, a computational model for indoor air quality numerical simulation, as well as a methodology to determine the outside airflow rate and to verify the indoor air quality in rooms, according to the European Standard CEN 1752. Also, the thermal comfort criteria for design of heating systems, the relationship between thermal environment and human performance, as well as the influence of carbon dioxide on human performance and productivity are presented. The performance of the developed computational models, implemented in two computer programs, is illustrated by using some numerical comparative applications for two building construction types. [Copyright &y& Elsevier]

Abstract: An archetypal simulation model of office building representing variability in UK office building stock by parameterising built form, construction elements, occupancy/usage and operational/control strategy has been developed thus enabling detailed energy performance simulation to be used for stock modelling and parametric studies. The paper discusses the building characteristics needed to be considered for energy performance simulation, their values, and how they can be used in parametric studies. These parameters include built forms, fabrics (including thermal mass and insulation positioning), glazing percentages and characteristics, daylight and solar control measures and activity and operational related parameters (heating and cooling set points, ventilation rate, occupancy density and metabolic rate, equipment and lighting gain). The default parameter values suggested for the archetypal simulation model reflect typical existing and currently proposed UK office building stock. An archetypal model, combined with parametric studies, can be used in assessing energy performance of building stock and evaluating adaptation/retrofitting strategies. [Copyright &y& Elsevier]