Zdenek Vana

Modeling and identification of a large multi-zone office building

Predictive control in buildings has undergone an intensive research in the past years. Model identification plays a central role in a predictive control approach. This paper presents a comprehensive study of modeling of a large multi-zone office building. Many of the common methods used for modeling of the buildings, such as a detailed modeling of the physical properties, RC modeling, etc., appeared to be unfeasible because of the complexity of the problem. Moreover, most of the research papers dealing with this topic presents identification (and control) of either a single-zone building, or a single building sub-system. On contrary, we proposed a novel approach combining a detailed modeling by a building-design software with a black-box subspace identification. The uniqueness of the presented approach is not only in the size of the problem, but also in the way of getting the model and interconnecting several computational and simulation tools.

Subspace identification of poorly excited industrial systems

Most of the industrial applications are multiple-input multiple-output (MIMO) systems, that can be be identified using knowledge of the systems physics or from measured data employing statistical methods. Currently, there is the only class of statistical identification methods capable of handling the issue of vast MIMO systems - subspace identification methods. These methods, however, as all statistical methods, need data of certain quality, i.e. excitation of corresponding order, no data corruption, etc. Nevertheless, the combination of statistical methods and physical knowledge of the system could significantly improve system identification. This paper presents a new algorithm which provides remedy to insufficient data quality of certain kind through incorporating of prior information, e.g. known static gain or input-output feedthrough. The presented algorithm naturally extends classical subspace identification algorithms, that is, it adds extra equations into the computation of system matrices. The performance of the algorithm is shown on a case study and compared to current methods, where the model is used for an MPC control of a large building heating system.

Predictive control oriented subspace identification based on building energy simulation tools

Even though modern control has emerged in numerous control applications, a building automation is still a field where the position of the classical control is almost exclusive. The main reason is that for the synthesis of a predictive controller a decent model for control is needed. In the field of building climate control, it is still problem to obtain a model of large building in an explicit form suitable for control. Most of the approaches either use building modeling software to get detailed model, which is unfortunately in implicit form; or the model is built-up as a first principle model, which usually ends-up as an extreme simplification of the reality. In this paper, a building model identification procedure is presented, wherein the building model is built-up as a first-principle model using a simulation software (detailed, precise, however in implicit form), and then a state-space model is identified by means of subspace identification methods. The main focus of the paper lays on a case study of a large office building, and the entire process of its identification.

Optimization of predicted mean vote thermal comfort index within Model Predictive Control framework

Recently, Model Predictive Control (MPC) for buildings has undergone an intensive research. Usually, according to the international standards, a static range for the air temperature represents the thermal comfort which is being kept making use of MPC while minimizing the energy consumption. On contrary, this paper deals with the optimization of the trade-off between energy consumption and Predicted Mean Vote (PMV) index which, opposed to the static temperature range, describes user comfort directly. PMV index is a nonlinear function of various quantities, which makes the problem more difficult to solve. The paper will show the main differences in MPC problem formulation, propose a tractable approximation strategy and compare the control performance both to the conventional and typical predictive control strategies. The approximation of PMV computation will be shown to be sufficiently precise and moreover, such a formulation keeps the MPC optimization problem convex. Finally, it will be shown that the proposed PMV based optimal control problem formulation shifts the savings potential of typical MPC by additional 10% while keeping the comfort at a desired level.

Notes on finding black-box model of a large building

Finding a suitable dynamic, linear, time-invariant model is a major obstacle for the use of model-predictive control of buildings. While finding models based on physical properties of the system is time consuming, statistical models need the system to be excited, which is not always possible. This paper presents possibilities for finding a suitable model based on subspace identification methods for unexcited data and data containing a specially designed identification experiment, and presents practical experiences gained while finding suitable models for a large building. Finally, some other possibilities of finding a model by incorporating prior information to the identification process are discussed, and the performance of the models is evaluated with respect to their use as a part of an MPC controller.

On predicted mean vote optimization in building climate control

Low energy buildings have been attracting much attention lately. Most of the research is focused on the building construction or alternative energy sources. Recently, there has been an intense research in the area of Model Predictive Control (MPC) for buildings. The main principle of such a controller 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, the thermal comfort is represented by a static range for the operative temperature according to the international standards. 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, however, is a nonlinear function of various quantities, which makes the problem more difficult to solve. The paper will show the main differences in MPC problem formulation, compare the control performance both to the conventional and predictive control strategies, point out that the proposed optimal control problem formulation shifts the savings potential of classical MPC by additional 11% and finally, the quality of the fulfillment of the thermal comfort will be addressed.

Usage of spot market prices prediction for demand side management

This paper discusses the issue of demand side management, in particular control of the output power of heat pumps based on the spot market electricity price. The main presumptions are the ability of controlled HVAC system to shift energy load on the customers side and sufficient credibility of an energy price prognosis on the electricity providers side. The paper first presents current situation in the Czech republic with electricity tariffs legislations, which have to be followed so that the proposed method is applicable in practice. It is shown that for successful implementation, it is required to have an energy load model, model of the accumulation as well as parameters of the heat pump. For the energy load model, statistical black box methods are used, while the other models are based on first principles. The models are together with the prediction of spot market price used within model predictive control framework resulting in cost savings higher than 10%.

Parameters identification of a chemical tank: A case study

Even though a modeling of a chemical tank is, in our case, in principle the modeling of a heat exchanger and belongs to the classical tasks, there are always some phenomena in practice, which are either difficult or impossible to include into the model. This especially holds in case of the old devices, where these parasitic events can have quite a large effect and can degrade both the model and the control strategy. The chemical tank being dealt within this paper suffers from many unpleasant phenomena, making it hard to come up with a proper identification method and model. In this paper, several modeling and identification approaches applied to the industrial type of shell and tube heat exchangers are presented. The goal is to show that less complex model serves the purpose better than a complex model and is also suitable for model based control. The control of a heat exchanger has been treated in many papers using some of the “classical” control concepts. In contrary, we propose a predictive control scheme, which compared to previous control approach have saved 25% of energy.

Identification and model selection of building models

Besides retrofitting, modernization and new ways of construction of the buildings, the cheaper and recently a very popular approach how to optimize energy consumption is to employ better control algorithms for the buildings. Predictive control has proven to be a strategy useful in many industries and became a suitable option for the building sector as well. The main bottleneck of this approach is a need for a fine model. There exist a number of building models and identification approaches. This paper provides a brief survey of the building modeling approaches and discusses their properties and applicability for the predictive control. Having a number of potential models at hand, the procedure of the model selection suitable for predictive control is presented. Finally, the performance of the model selection procedure is examined in a two zone building. The results are then presented and the conclusions drown.