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Model Predictive Control of Thermally Active Building Systems and Monitoring of two Test-Boxes

A robust, predictive controller which utilises weather forecast data to control thermally active building systems is designed, researched and assessed in terms of energy efficiency and comfort compared to standard controllers, especially for cooling purposes. Simulations and real measurements using two "Test-Boxes" with thermally active building systems which are constructed and built for this purpose are used to analyse energy efficiency and comfort. Low complexity and transparency of methods and solutions should allow for transferability of all results to guarantee maximum usability for similar applications.

Short Description

Status

ongoing

Summary

Starting point/motivation

Thermally Active Building Systems (TABS) can be utilised as short-term storage for heat or coolness. Increasing cooling demand for air conditioning leads to extended use of refrigerators in broad daylight. In general, high outside temperatures have an adverse effect on the refrigerator efficiency, the economic costs, and cause peak loads in the grid. In addition, district heat main supply pipes operating at their limit often prevent further extension of district heating in remote areas. Reduction of peak load in buildings through temporal extension of coolness- and heat delivery throughout the day and night requires a predictive control concept which makes use of weather forecast data. Such a controller also eases the utilization of renewable energy.

The enormous complexity of existing approaches to predictive building automation prevents this technology from being used in practice. Although it has been investigated in a number of research projects, official numbers as to how much energy can be saved are not yet available. On the other hand, practitioners claim that much energy is saved with the use of  predictive control, but the quality of the use of weather forecast data in practice is very low. A standardised method for thermal predictive control of TABS is lacking, as is an experimental framework for the investigation of various controllers for this purpose.

Contents and goals

The aim of this project is to build two "Test-Boxes" to be placed in real ambient conditions and successfully apply a predictive controller for a TABS in one Test-Box. The given constraints are: efficient energy utilisation and maximisation of comfort and robustness. A predictive controller utilising a simple model of the control process and weather forecast data as input data is planned and realised for this purpose.

Methods of treatment

A complex model (e.g. CFD-simulation) will be developed and used to derive a simple model, which is required for the predictive controller. In addition the complex model assists in optimising the position of sensors, which will be used to frequently assimilate the simple model during the operation of the predictive controller in real time. The second Test-Box serves as a reference case. To monitor actual values, the Test-Boxes and the TABS will be equipped with a number of sensors. Maximum accuracy for in situ weather data will be attained by placing a weather station close to the Test-Boxes; obtained data will be used to investigate different approaches for in situ weather forecast optimisation.

Expected results/conclusions

A major result is predictive energy efficient heating and cooling by means of a TABS without any loss of comfort (undercooling). One aim is the reduction of energy demand - required for cooling - by 10% for the predictive controlled Test-Box. The project should provide insight into parameterisation rules for a predictive controller for TABS dependent on easily available physical parameters. Another important result is the identification of an optimal position for the sensors for the purpose of model assimilation, to allow for a simple thermal model as part of the predictive controller.

The project aims for maximum transferability of findings, to assure usability for similar applications in different environ­ments. The final aim is to provide insight with respect to exergetic advantages of a predictive controller and a cost-efficient method for in situ weather forecasting.

Project Partners

Project management

Graz University of Technology, Institute of Thermal Engineering
Dipl.-Ing. Dr.mont. Hermann Schranzhofer, Mag.rer.nat. M.Sc. Ing. Martin Pichler

Project or cooperation partner

Contact Address

Graz University of Technology, Institute of Thermal Engineering
Dipl.-Ing. Dr.mont. Hermann Schranzhofer
Inffeldgasse 25b
A-8010 Graz
Tel.: +43 (316) 873 7314
Fax: +43 (316) 873 7305
E-Mail: hermann.schranzhofer@tugraz.at
Web: www.iwt.tugraz.at

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