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Statistical Learning of MPC Control Laws for Buildings

Author(s):

S. Deml
Conference/Journal:

Automatic Control Laboratory, ETH Zürich, Semester Thesis, FS13 (10251)
Abstract:

This study investigates machine learning methods for model predictive control laws in the field of building control. It focuses on continuous input types of the HVAC system during a summer period with deterministic occupancy information. A general learning setup is introduced and an approach for sample generation is shown. Furthermore a simple algorithm for the optimal control inputs for blinds and electric lighting is derived, while for the cooling input a more generic approach is presented. Subsequently the closed-loop performance of the machine learned controller (MLC) is discussed and a comparison to other MPC implementations is made. In order to increase the need for predictive control the available cooling power has been limited, leading to significant pre-cooling in the morning hours. It is shown that even for this advanced control problem the presented learning approach leads to very convincing results which are close to optimal. Finally, a sensitivity analysis of the learned parameters shows that the controller also performs convincingly for changes in the weather data.

Year:

2013
Type of Publication:

(13)Semester/Bachelor Thesis
Supervisor:

G. Schildbach

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% Autogenerated BibTeX entry
@PhdThesis { Xxx:2013:IFA_4639
}
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