Holistic optimization of HVAC systems via distributed data-driven control

In the present paper, the control design of Heating, Ventilation and Air Conditioning (HVAC) systems is investigated. In largescale 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.

[1]  Petru-Daniel Morosan,et al.  A distributed MPC strategy based on Benders' decomposition applied to multi-source multi-zone temperature regulation , 2011 .

[2]  Manfred Morari,et al.  Model predictive control: Theory and practice , 1988 .

[3]  K Katsigarakis,et al.  AN EVENT-DRIVEN SOA-BASED PLATFORM FOR ENERGY- EFFICIENCY APPLICATIONS IN BUILDINGS , 2013 .

[4]  Lukas Ferkl,et al.  Model predictive control of a building heating system: The first experience , 2011 .

[5]  Andrew W. Moore,et al.  Locally Weighted Learning for Control , 1997, Artificial Intelligence Review.

[6]  Elias B. Kosmatopoulos,et al.  Adaptive-fine tuning of building energy management systems using co-simulation , 2012, 2012 IEEE International Conference on Control Applications.

[7]  Dimitrios V. Rovas,et al.  Black-Box Optimization for Buildings and Its Enhancement by Advanced Communication Infrastructure , 2013 .

[8]  Nikolaus Hansen,et al.  Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[9]  Agis M. Papadopoulos,et al.  Cost-optimal insulation thickness in dry and mesothermal climates: Existing models and their improvement , 2014 .

[10]  Tomo Cerovsek,et al.  A review and outlook for a 'Building Information Model' (BIM): A multi-standpoint framework for technological development , 2011, Adv. Eng. Informatics.

[11]  Christopher K. I. Williams,et al.  Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) , 2005 .

[12]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[13]  Manfred Morari,et al.  Model predictive control: Theory and practice - A survey , 1989, Autom..

[14]  Jana Trojanova,et al.  From Symptoms to Faults: Temporal Reasoning Methods , 2009, 2009 International Conference on Adaptive and Intelligent Systems.

[15]  Pei Zhou,et al.  Demand-based temperature control of large-scale rooms aided by wireless sensor network: Energy saving potential analysis , 2014 .

[16]  V. Smidl,et al.  Distributed Bayesian Decision-Making for Urban Traffic Control , 2006, IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics.

[17]  Arnaud G. Malan,et al.  HVAC control strategies to enhance comfort and minimise energy usage , 2001 .

[18]  A. Charnes,et al.  Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis , 1984 .

[19]  Miroslav Kárný,et al.  Axiomatisation of fully probabilistic design , 2012, Inf. Sci..