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Holistic optimization of HVAC systems via distributed data-driven control

 
: Macek, K.; Rovas, D.; Schmidt, M.; Valmaseda, C.C.

Abraham, Ajith P. (Ed.) ; International Association for Development of the Information Society -IADIS-:
8th Multi Conference on Computer Science and Information Systems, MCCSIS 2014 : 15-19 July, Lisbon, Portugal; Proceedings of 8th European Conference on Data Mining 2014 and 8th International Conference Intelligent Systems and Agents 2014
Lisbon: IADIS Press, 2014
ISBN: 978-989-8704-10-8
pp.75-82
Multi Conference on Computer Science and Information Systems (MCCSIS) <8, 2014, Lisbon>
European Conference on Data Mining (DM) <8, 2014, Lisbon>
International Conference Intelligent Systems and Agents <8, 2014, Lisbon>
English
Conference Paper
Fraunhofer IBP ()

Abstract
In the present paper, the control design of Heating, Ventilation and Air Conditioning (HVAC) systems is investigated. In large-scale buildings - e.g. hotels or hospitals - The high dimension of the control design problem precludes a solution with reasonable computational effort. In this paper, a distributed control strategy is proposed, where interacting agents are operating sub-systems; interaction between these agents can ensure that an optimum solution can be obtained. A novel method to distributed control tis introduced based on data-driven modeling where the strategy is not based on explicit optimization, but on weighted learning of the control rules; two examples of the addressed system are formulated. A significant advantage of the proposed approach consists in minimal assumptions on the addressed system and the most significant disadvantage is the need of sufficiently rich data-sets.

: http://publica.fraunhofer.de/documents/N-351007.html