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Learning Near-optimal Decision Rules for Energy Efficient Building Control


A. Domahidi, F. Ullmann, M. Morari, C.N. Jones

IEEE Conference on Decision and Control, Maui, HI, USA, pp. 7571 - 7576

While recent studies suggest that advanced optimization based control methods such as model predictive control (MPC) can significantly increase energy efficiency of buildings, adoption of these sophisticated control approaches by the industry is still slow for mainly two reasons: First, MPC requires a significantly more complex IT infrastructure, and second, building operators employ simple controllers based on intuitive decision rules, which can be tuned easily on-site. In this paper, we suggest a synthesis procedure for rule based controllers which extracts prevalent information from simulation data with MPC controllers to construct a set of human readable rules while preserving much of the control performance. The method is based on the well known AdaBoost algorithm from the field of machine learning. We consider also the ranking and selection of measurements which are used for a decision and show that this feature selection is useful in both complexity reduction and reduction of investment costs by pruning unnecessary sensors. The proposed method is evaluated in simulation for six different case studies and is shown to maintain the high performance of MPC despite the tremendous reduction in complexity.


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% Autogenerated BibTeX entry
@InProceedings { DomEtal:2012:IFA_4035,
    author={A. Domahidi and F. Ullmann and M. Morari and C.N. Jones},
    title={{Learning Near-optimal Decision Rules for Energy Efficient
	  Building Control}},
    booktitle={IEEE Conference on Decision and Control},
    pages={7571 -- 7576},
    address={Maui, HI, USA},
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