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Recently, in course of the last development in information technology, control algorithms, models and simulators of buildings management, an essential role in given to 3D information modeling BIM in order to facilitate, improve and expand structure related services. A building model is generated combining images taken from different angles. The share of aerial photo acquisition is noticeably augmenting. Chronologically, this has started since the migration of the unmanned vehicle technologies to the civil market. As a result, a generation of sophisticated aerial machines equipped with high-resolution cameras can provide photographs of a surface taken from different angles allowing generating fairly accurate 3D models. The risk analysis shows that the human factor represents the biggest probable technical disturbance in the image acquisition process. Hence companies dealing with such projects try to minimize the role of the human-operator by automating the workflow. Automated aerial photo acquisition can be a solution. The quality of 3D building model, that we are trying to present in this paper, is directly related to the optimized usage, flight stability and the efficiency of the electronics. From a robotic point of view, reliable positioning of unmanned machine is an up-to-date technical task. In this paper, we will be identifying the quadrotor as an unmanned aerial vehicle performing photogrammetry. Such model was chosen because of its size, cost, maintenance-friendly and most important its maneuverability. On the other hand, the quadrotor does not offer long flight range and it is not stable. In order to improve the stability we suggest to identify and eliminate the noises acquired by sensors readings, and, on the other hand, to develop adaptive fuzzy logic position controllers to deal with the external wind factor. The effectiveness of the stabilization systems is endorsed with simulation and experimental results. [ABSTRACT FROM AUTHOR]

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]

It is important to have practical methods for constructing and learning a good mathematical model for a building’s thermal system in the presence of unmeasured disturbances and using data from closed loop operation. With this goal in mind, this paper presents a mathematical framework that explains the asymptotic behavior of an estimated model under those conditions and that can aid in learning an accurate model. Some analytic results from the literature of system identification are extended and interpreted for building systems. A new identification approach for determining an accurate thermal network (RC) model for a multi-zone building is developed based on the analytic result, and its superior performance over a conventional grey-box modeling approach is demonstrated experimentally. [ABSTRACT FROM AUTHOR]

In this paper an effective modeling approach for building heating dynamics is presented. The proposed model is a so-called fuzzy black-box model. This data-based model is able to reproduce the nonlinear dynamic behavior of a complex office building split into different zones. The fuzzy black-box model is based on local linear models (LLM), for which a 2-dimensional partition space is obtained. In the proposed work data pre-processing is one of the most important tasks and thus extensively explained. This includes the selection of suitable input and output data and selection of appropriate model orders. Furthermore, it is presented that even with a small amount of data good models can be achieved. The output of this LLM algorithm is an autoregressive model with exogenous input (ARX). The resulting model is specifically tailored for model-based control design. The uniqueness of the presented work lies not only in the low-order of the building model, but also in the way of retrieving a model in the most effective and time-saving way. [ABSTRACT FROM AUTHOR]

Minimizing energy consumption of heating, ventilation and air conditioning (HVAC) systems in buildings has experienced an increasing attention recently. Mainly motivated by the exploitation of building automation systems (BAS), as well as simulation tools, innovative practices and methodologies have been introduced to reduce the costs of energy required for heating and air conditioning of buildings. Outstanding has been the establishment of model predictive control (MPC) as a control strategy for the optimal operation of HVAC systems. The basis for MPC is a dynamic model, which is objectively challenging and time-consuming to obtain. This paper presents two modeling approaches of the return water temperature of a central chiller plant based on data of the real operation of a building, weather disturbances, and the temperature of a reference thermal zone. It integrates building's real measurements with a room simulator model. In this work, an original method for accurately describing the cooling dynamics of a case study hotel on a tropical island is proposed. [ABSTRACT FROM AUTHOR]

Modern control methods such as Model Predictive Control (MPC) are getting popular in recent years in many fields of industry. One of the branches that have witnessed great increase of interest in use of the MPC over the last few years is the building climate control area. According to the studies, the energy used in the building sector counts for 20 – 40 % of the overall energy consumption. Almost half of this amount consists of heating, ventilation and air-conditioning (HVAC) costs which implies that energy consumption decrease in this area is one of the most interesting challenges today. Besides enormous potential in reduction of energy consumed by heating, ventilation and air conditioning (HVAC) systems brought by such controller, it suffers from a bottleneck being the necessity of having a reliable mathematical model of the building at disposal. By finding a mathematical model appropriate for the MPC, it is meant to obtain such a model that is able to predict the behavior of the building sufficiently accurately for several hours ahead, which is an especially delicate task. This task is getting even more complicated in case of a real-life application. In this paper, we are looking for a reliable model of a huge three-storey office building in Hasselt, Belgium. For parameter estimation, an advanced identification approach is used – its advantage is that it attacks the problem of minimization of multi-step prediction error and in this way, it corresponds to MPC requirements for a good multi-step predictor. Moreover, we discuss not only the identification approach itself but we also focus on accompanying problems with real-operation data acquisition, processing and special treatment which is an indispensable step for achieving satisfactory identification results. The chosen model is now used in real operation with MPC at Hollandsch Huys. [ABSTRACT FROM AUTHOR]

[Display omitted] In this paper, we propose a data-driven methodology to identify the optimal placement of sensors in a multi-zone building. The proposed methodology is based on statistical tests that study the (in) dependence of measurements from various available sensors. The tests advice on a set of most dissimilar sensors to be retained, as they would convey the maximum information. The method starts with an initial setup that can provide measurements of every building zone to carry out this study; any of these sensors can be removed eventually to decrease costs in normal operation. The method has the advantages of being purely data driven and computationally efficient, as against several methods proposed in the scientific literature, that operate under the premise that detailed building models are available, to evaluate the number/position of the required sensors. This property makes the method scale to different buildings, in an expert free manner. The methodology can help towards better characterization of a building for optimal control and monitoring applications. It is validated against a widely used method – Kalman filtering with Grey-box models, using two different case studies. In both cases, the proposed approach agrees with the results using grey box models, suggesting that the method is reliable, while being quick and efficient. [ABSTRACT FROM AUTHOR]