Black-Box Optimization for Buildings and Its Enhancement by Advanced Communication Infrastructure
Abstract The solution of repeated fixed-horizon trajectory optimization problems of processes that are either too difficult or too complex to be described by physics-based models can pose formidable challenges. Very often, soft-computing methods e.g. black-box modeling and evolutionary optimization are used. These approaches are ineffective or even computationally intractable for searching high-dimensional parameter spaces. In this paper, a structured iterative process is described for addressing such problems: the starting point is a simple parameterization of the trajectory starting with a reduced number of parameters; after selection of values for these parameters so that this simpler problem is covered satisfactorily, a refinement procedure increases the number of parameters and the optimization is repeated. This continuous parameter refinement and optimization process can yield effective solutions after only a few iterations. To illustrate the applicability of the proposed approach we investigate the problem of dynamic optimization of the operation of HVAC (heating, ventilation, and air conditioning) systems, and illustrative simulation results are presented. Finally, the development of advanced communication and interoperability components is described, addressing the problem of how the proposed algorithm could be deployed in realistic contexts.
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K. DORVEL, S. MEDVED, Multi-objective optimization of a building free cooling system, based on weather prediction, Energy and Buildings, 52 (2012), 99-106
M. J. GANCO, R. ALCALA, F.HERRENA, A multi-objective evolutionary algorithm for an effective tuning of fuzzy logic controllers in heating, ventilating and air conditioning systems, Applied Intelligence, 36 (2) (2012), 330-347
GUYON, I., ELISEEFF, A., An introduction to variable and feature selection, The Journal of Machine Learning Research, 3 (2003) 1157-1182
N. HANSEN, A. OSTERMEIER, Adapting arbitrary normal mutation distributions in evolution strategies: The covariance matrix adaptation. in: Proceedings of IEEE International Conference on Evolutionary Computation, Nagoya, Japan, 1996, pp. 312-317
P. HAVES, B. HENCEY, F. BORELLI, J. ELLIOT, Y. MA, B. COFFEY, S. BENGEA, M. WETTER, Model predictive control of HVAC systems: implementation and testing at the University of California, Merced, technical report, Lawrence Berkeley National Laboratory, Berkeley, California, 2010.
G. P. HENZE, S. LIU, Experimental analysis of simulated reinforcement learning control for active and passive building thermal storage inventory: Part 1. theoretical foundation, Energy and Buildings, 38 (2) (2006), 142-147
M. HU, J. D. JEFFERY, T.WU, Peak load shifting control using a mimetic algorithm, European Journal on Operational Research, 217 (1) (2012), 185-197
ISO 16739 (2013). Industry Foundation Classes (IFC) for data sharing in the construction and facility management industries. The International Organization for Standardization
J. KENNEDY, R. C. EBERHART, Particle swarm optimization, in: IEEE International Conference on Neural Networks, Piscataway, New Jersey, 1995, pp. 1942-1948
G. KONTES, G. GIANNAKIS, E. KOSMATOPOULOS, D.V. ROVAS, Adaptive fine-tuning of building energy management systems using co-simulation, in Proceedings: 2012 IEEE International Conference on Control Applications, Dubrovnik, Croatia, 2012, pp. 1664-1669.
A. K. KORDON, Applied computational intelligence – how to create value, Springer, Berlin Heidelberg, 2007.
K. MACEK, K. MACÍK, A methodology for quantitative comparison of control solutions and its application to HVAC (heating, ventilation and air conditioning) systems, Energy, 44 (1) (2012) 117-125
K. MACEK, V. BICÍK, J. ROJÍCEK, Trajectory optimization under changing conditions through evolutionary approach and black-box models with refining, Advances in Intelligent Systems and Computing 217 (2013) 267-274
K. MACÍK, J. ROJÍCEK, P. STLUKA, J. VASS, Advanced HVAC Control: Theory vs. Reality, in: Preprints of the 18th IVAC World Congress, Milano, Italy, 2011, pp. 3108-3113
F. OLDEWURTEL, A. PARISIO, C. N. JONES, D. GYALISTRAS, M. GWERDER, V. STAUCH, B. LEHMANN, M. MORARI, Use of model predictive control and weather forecasts for energy efficient building climate control, Energy and Buildings, 45 (2012), 15-27
S.J. QIN, T.A. BADGWELL, A survey of industrial model predictive control technology Control Engineering Practice, 11 (2003) 733–764
C. RASMUSSEN, C. K. I. WILLIAMS, Gaussian processes for machine learning, The MIT Press, Cambridge, Massachusetts, 2006
R. ROJAS, Neural networks – a systematic introduction, Springer, Berlin, 1996
J. ROJÍCEK, R. FIŠERA, G.D. KONTES, G.I. GIANNAKIS, D.V. ROVAS, Functional and technological definition of BIM-aware services to Assess, Predict and Optimize energy performance of buildings. In Proceedings 2nd Central European Symposium on Building Physics, Vienna, Austria, September 9-11, 2013. (To Appear)
R. STORN, K. PRICE, Differential evolution – a simple and efficient heuristic for global optimization, Journal of Global Optimization, 11 (1997), 341-359
M. STRELEC, K. MACEK, A. ABATE, Modeling and simulation of a microgrid as a stochastic hybrid system, in: Proceedings of the IEEE PES Innovative Smart Grid Technologies, Berlin, Germany, 2012, pp. 1-9.
C. VALMACEDA, K. KATSIGARAKIS, M.A. GARCIA FUENTES, J. L. HERNANDEZ GARCIA, G.D. KONTES, D.V. ROVAS, An event-driven SOA-based platform for energy-efficiency applications in buildings, CIBW78 2013, Beijing, China (To appear).
L. WASSERMAN, All of nonparametric statistics, Springer, New York, 2006
S. WOLD, Principal component analysis, Chemometrics and Intelligent Laboratory Systems, 2 (1) (1997), 37-52
E. ZACEKOVA, L. FERKL, Building modeling and control using multi-step ahead error minimization, in: Proceedings of Mediterranean Conference Control & Automation, Barcelona, Spain, 2012, pp. 421-426.
M. J. GANCO, R. ALCALA, F.HERRENA, A multi-objective evolutionary algorithm for an effective tuning of fuzzy logic controllers in heating, ventilating and air conditioning systems, Applied Intelligence, 36 (2) (2012), 330-347
GUYON, I., ELISEEFF, A., An introduction to variable and feature selection, The Journal of Machine Learning Research, 3 (2003) 1157-1182
N. HANSEN, A. OSTERMEIER, Adapting arbitrary normal mutation distributions in evolution strategies: The covariance matrix adaptation. in: Proceedings of IEEE International Conference on Evolutionary Computation, Nagoya, Japan, 1996, pp. 312-317
P. HAVES, B. HENCEY, F. BORELLI, J. ELLIOT, Y. MA, B. COFFEY, S. BENGEA, M. WETTER, Model predictive control of HVAC systems: implementation and testing at the University of California, Merced, technical report, Lawrence Berkeley National Laboratory, Berkeley, California, 2010.
G. P. HENZE, S. LIU, Experimental analysis of simulated reinforcement learning control for active and passive building thermal storage inventory: Part 1. theoretical foundation, Energy and Buildings, 38 (2) (2006), 142-147
M. HU, J. D. JEFFERY, T.WU, Peak load shifting control using a mimetic algorithm, European Journal on Operational Research, 217 (1) (2012), 185-197
ISO 16739 (2013). Industry Foundation Classes (IFC) for data sharing in the construction and facility management industries. The International Organization for Standardization
J. KENNEDY, R. C. EBERHART, Particle swarm optimization, in: IEEE International Conference on Neural Networks, Piscataway, New Jersey, 1995, pp. 1942-1948
G. KONTES, G. GIANNAKIS, E. KOSMATOPOULOS, D.V. ROVAS, Adaptive fine-tuning of building energy management systems using co-simulation, in Proceedings: 2012 IEEE International Conference on Control Applications, Dubrovnik, Croatia, 2012, pp. 1664-1669.
A. K. KORDON, Applied computational intelligence – how to create value, Springer, Berlin Heidelberg, 2007.
K. MACEK, K. MACÍK, A methodology for quantitative comparison of control solutions and its application to HVAC (heating, ventilation and air conditioning) systems, Energy, 44 (1) (2012) 117-125
K. MACEK, V. BICÍK, J. ROJÍCEK, Trajectory optimization under changing conditions through evolutionary approach and black-box models with refining, Advances in Intelligent Systems and Computing 217 (2013) 267-274
K. MACÍK, J. ROJÍCEK, P. STLUKA, J. VASS, Advanced HVAC Control: Theory vs. Reality, in: Preprints of the 18th IVAC World Congress, Milano, Italy, 2011, pp. 3108-3113
F. OLDEWURTEL, A. PARISIO, C. N. JONES, D. GYALISTRAS, M. GWERDER, V. STAUCH, B. LEHMANN, M. MORARI, Use of model predictive control and weather forecasts for energy efficient building climate control, Energy and Buildings, 45 (2012), 15-27
S.J. QIN, T.A. BADGWELL, A survey of industrial model predictive control technology Control Engineering Practice, 11 (2003) 733–764
C. RASMUSSEN, C. K. I. WILLIAMS, Gaussian processes for machine learning, The MIT Press, Cambridge, Massachusetts, 2006
R. ROJAS, Neural networks – a systematic introduction, Springer, Berlin, 1996
J. ROJÍCEK, R. FIŠERA, G.D. KONTES, G.I. GIANNAKIS, D.V. ROVAS, Functional and technological definition of BIM-aware services to Assess, Predict and Optimize energy performance of buildings. In Proceedings 2nd Central European Symposium on Building Physics, Vienna, Austria, September 9-11, 2013. (To Appear)
R. STORN, K. PRICE, Differential evolution – a simple and efficient heuristic for global optimization, Journal of Global Optimization, 11 (1997), 341-359
M. STRELEC, K. MACEK, A. ABATE, Modeling and simulation of a microgrid as a stochastic hybrid system, in: Proceedings of the IEEE PES Innovative Smart Grid Technologies, Berlin, Germany, 2012, pp. 1-9.
C. VALMACEDA, K. KATSIGARAKIS, M.A. GARCIA FUENTES, J. L. HERNANDEZ GARCIA, G.D. KONTES, D.V. ROVAS, An event-driven SOA-based platform for energy-efficiency applications in buildings, CIBW78 2013, Beijing, China (To appear).
L. WASSERMAN, All of nonparametric statistics, Springer, New York, 2006
S. WOLD, Principal component analysis, Chemometrics and Intelligent Laboratory Systems, 2 (1) (1997), 37-52
E. ZACEKOVA, L. FERKL, Building modeling and control using multi-step ahead error minimization, in: Proceedings of Mediterranean Conference Control & Automation, Barcelona, Spain, 2012, pp. 421-426.
Macek, K., Rojicek, J., Kontes, G., & Rovas, D. V. (2013). Black-Box Optimization for Buildings and Its Enhancement by Advanced Communication Infrastructure. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 2(2), 53–64. https://doi.org/10.14201/ADCAIJ2013255364
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