Search

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]

The objective of this work was to evaluate the interactions of users with air conditioning systems, lighting, natural ventilation, internal shading devices, and costs related to automation of a teaching building. Three behavior patterns (proactive, intermediate and careless) were created. The proactive user seeks to use natural light, uses passive solar shading and integration of natural and artificial ventilation. The intermediate user does not seek integration of natural and artificial lighting, they use hybrid ventilation as a ventilation strategy. The careless user does not use any passive strategy, predominantly uses artificial conditioning and adjusts cooling and heating setpoint to values out of the norms. In addition to the users' behaviors, the automated building was modeled. Computer simulations were performed in the EnergyPlus program, in order to measure the influence of behavior patterns on the building's energy consumption. Based on the analysis of the building's energy consumption profile, results indicate users are between the intermediate and the careless user classification, with a tendency to the intermediate user profile. The paper also presents an analysis of the payback time with the building automation in face of the user profiles. [ABSTRACT FROM AUTHOR]