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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]

Abstract: Model predictive control has become a widespread solution in many industrial applications and is gaining ground in the field of energy management and automation systems of buildings. A model with reasonable prediction properties is an ultimate condition for good performance of the predictive controller. This paper presents an approach in which a model of a building is selected by an iterative two stage procedure. In the first stage, a minimum set of disturbance inputs is formed so that the resulting model is the best with respect to a defined quality criterion; then the second stage comprises addition of the states to obtain the final minimum set of states maximizing the model quality. The procedure stops when it makes no sense to select more complex model as it brings no more quality improvements. Statistical tests such as the likelihood ratio test, the tests based on cumulative periodogram, the two-sample Kolmogorov–Smirnov test as well as others (fit factor and coefficient of determination) are used to evaluate the relationship between the addition of inputs/states and the model quality. Three identification approaches, namely model predictive control relevant identification, deterministic semi-physical and probabilistic semi-physical modeling are used for estimation of building parameters. Finally, a case study is provided where all the above mentioned approaches are investigated and tested. [Copyright &y& Elsevier]

Abstract: Recently, there has been an intensive research in the area of Model Predictive Control (MPC) for buildings. The key principle of MPC is a trade-off between energy savings and user welfare making use of predictions of disturbances acting on the system (ambient temperature, solar radiation, occupancy, etc.). Usually, according to international standards, the thermal comfort is represented by a static range for the operative temperature. By contrast, this paper is devoted to the optimization of the Predicted Mean Vote (PMV) index which, opposed to the static temperature range, describes user comfort directly. PMV index is, however, a nonlinear function of various quantities, which limits the applicability and scalability of the control problem formulation. At first, PMV-based formulation is stated, the main differences between typical MPC problem formulation and PMV based formulation are outlined, a computationally tractable approximation of the nonlinear optimal control problem is presented and its accuracy is validated. Finally, control performance is compared both to a conventional and predictive control strategies and it turns out that the proposed optimal control problem formulation shifts the savings potential of typical MPC by additional 10–15% while keeping the comfort within levels defined by standards. [Copyright &y& Elsevier]

Abstract: Low energy buildings have attracted lots of attention in recent years. Most of the research is focused on the building construction or alternative energy sources. In contrary, this paper presents a general methodology of minimizing energy consumption using current energy sources and minimal retrofitting, but instead making use of advanced control techniques. We focus on the analysis of energy savings that can be achieved in a building heating system by applying model predictive control (MPC) and using weather predictions. The basic formulation of MPC is described with emphasis on the building control application and tested in a two months experiment performed on a real building in Prague, Czech Republic. [Copyright &y& Elsevier]