Built Environment

Model predictive building control

Duration: 24 Monate (01.01.2016 – 31.12.2017)

Project volume: 12 847 €

Funder: DAAD, Programm Projekt bezogener Personenaustausch mit Australien

 

Funded doctoral students:M.Eng. Katharina Boudier, M.Sc. Raghuram Kalyanam, M.Eng. Mathias Kimmling, M.Sc. Abolfazl Ganji

 

Short Description

The transparent surface of the building envelope (windows, glass facades) has a significant impact on the indoor climate in buildings. The condition of windows is decisive for the thermal and visual comfort of the room users as well as for the air quality in the interior. Of course, window surfaces have a strong influence on the cooling and heating loads in the building and are therefore an excellent starting point for reducing energy consumption.

The use of so-called "Complex Fenestration Systems" (CFS) (i.e. glazing in combination with shading systems, switchable electrochromic glazing, angle-dependent systems, etc.) allows intelligent light and solar radiation management. The optimized operation of window openings and the intelligent control of such CFS is the basis for high comfort and maximum energy savings.

Due to the complex dynamic interactions of the heat and mass flows in buildings and the strongly varying outdoor climate as well as the time-variant use, the development of a model predictive control for buildings is not trivial.

The aim of the project is to develop and validate control algorithms. These should make optimized real-time decisions when operating controllable windows and CFS. The main goals in the project are:

1. Development of simulation-based model predictive approaches (hereinafter also referred to as MPC - model predictive control) for the prediction and optimization of building behavior. The algorithms should embed learning and adaptation measures to map the differences between deterministic assumptions and stochastic variables (e.g. occupant behavior through feedback).

2. Investigation of the performance of control algorithms with measured and/or assumed input variables. The aim is a sensitivity analysis, if e.g. B. high-resolution variables can be controlled with low-resolution weather forecasts. Possible energy savings are to be quantified

3. Validation of the developed control algorithms on test buildings in Australia and Germany. For this purpose, an excellent Solar Decathlon building is available in Australia as well as the institute building of the Australian partner, which is equipped with comprehensive building automation. In Germany, the project manager is currently setting up the Living Lab Smart Office (opening October 2015). The aim for both partners is to validate the developed MPC approach using their own existing test buildings and to transfer it to the buildings in the partner country during the exchange phase. This puts the stability of the models under variable boundary conditions to the test.

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