Aggregated Performance and Qualitative Modeling Based Smart Thermal Control

Afef Denguir, Francois Trousset, Jacky Montmain

2014

Abstract

In order to ensure thermal energy efficiency and follow government’s thermal guidance, more flexible and efficient buildings’ thermal controls are required. This paper focuses on proposing an efficient, scalable, reusable, and data weak dependent smart thermal control approach based on an aggregated performance and imprecise knowledge of buildings’ thermal specificities. Its main principle is to bypass data unavailability and quantitative models identification issues and to ensure an immediate thermal enhancement. For this, we propose, first, an aggregated performance based smart thermal control in order to identify relevant thermal setpoints. An extended thermal qualitative model is then introduced to guarantee an efficient achievement of the identified thermal setpoints. Uncertainty about how relevant a thermal control is for a given thermal situation is thus reduced using online and preference based learnings.

References

  1. Aydinalp, M., Ugursal, V.I., Fung, A.S., 2002. Modeling of the appliance, lighting, and sapce-cooling enrgy consumptions in the residential sector using neural networks, Applied Energy, vol. 2, n°71, pp. 87-110.
  2. Calvino, F., Gennusca, M.L., Rizzo, G., Scaccianoce, G., 2004. The control of indoor thermal comfort conditions: introduicing a fuzzy adaptive controller, Energy and Buildings, vol. 36, pp. 97-102.
  3. Denguir, A., Trousset, F., Montmain, J., 2012. Comfort as a Multidimensional Preference Model for Energy Efficiency Control Issues, In SUM 2012, pp. 486-499.
  4. Denguir, A., Trousset, F., Montmain, J., 2014. Approximate Reasoning for an Efficient, Scalable and Simple Thermal Control Enhancement, IN IPMU, Montpellier.
  5. Dong, B., Cao, C., Lee, S.E., 2005. Applying support vector machines to predict building energy consumption in tropical region,Energy and Buildings, vol. 5, n°37, pp. 545-553.
  6. Dounis, A.I., Santamouris, M.J., Lefas, C.C,. Argirious, A., 1995. Design of a fuzzy set environment comfort system, Energy and Buildings, vol. 1, n°22, pp. 81-87.
  7. Dubois, D., 1989. Order of magnitude reasoning with fuzzy relations, Artificial Intelligence, vol. 3, n°14, pp. 69-94.
  8. European Parliament and Council (EP&C)., 2012. Directive 2012/27EU of European Parliament and Council of 27 October 2012 on the energy performance of buildings, Official Journal of the European Union, pp. 1-56.
  9. Fanger, P.O., 1967, Calculation of Thermal Comfort: Introduction of a basic Comfort Equation. ASHRAE Trans. Vol. 73.
  10. Fishburn, P.C. 1970. Utility Theory for Decision-Making. John Wiley & Sons, New York.
  11. Gagge, A.P., Fobelets, A.P., Berglund, L.G., 1986. A Standard Predictive Index of Human Response to the Thermal Environment. ASHRAE Trans, vol. 92.
  12. Grabisch, M. k-Ordered Discrete Fuzzy Measures and Their Representation. In: Fuzzy sets and systems, vol. 92, pp. 167-189. (1997).
  13. Kalogirou, S.A., Bojic, M., 2000. Artificial neural networks for the prediction of the energy consumption of a passive solar building, Energy, vol. 5, n°25, pp. 479-491.
  14. Krantz, D.H., Luce, R.D., Suppes, P., Tversky, A. 1971. Foundations of measurement, Additive and Polynomial Representations, vol. 1. Academic Press.
  15. Kuipers, B., 1986. Qualitative simulation, Artificial Intelligence, vol. 29, pp. 289-388.
  16. Labreuche, C.2011. Construction of a Choquet integral and the value functions without any commensurateness assumption in multi-criteria decision making. In EUSFLAT-LFA, Aix-les-Bains, France.
  17. Li, Q., Meng, Q.L., Cai, J.J., Hiroshi, Y., Akashi, M., 2009. Applying support vector machine to predict hourly cooling load in the building, Applied Energy, vol. 10, n°86, pp. 2249-2256.
  18. Ma, Y., Borrelli, F., Hencey, B., Packard, A., Bortoff, S., 2009. Model predictive control of thermal energy storage in building cooling systems. In Conference on Decision and Control and Chinese Control Conference, pp. 392-397.
  19. MQ&D coordinated by P.Dague, 1995. Qualitative Reasoning: a survey of techniques and applications, AI Communications The European Journal of AI, IOS Press, vol. 8, pp. 119-192.
  20. NF EN ISO 7730, 2006. Ergonomie des ambiances thermiques : Détermination analytique et interprétation du confort thermique à l'aide de calculs des indices PMV et PPD et du confort thermique local, AFNOR.
  21. Oldewurtel, A., Parisio, A., Jones, C.N., Morari, M., Gyalistras, D., Gwerder, M., 2010. Energy efficient building climate control using stochastic model predictive control and weather predictions, In American Control Conference, Baltimore.
  22. Pacific Northwest National Laboratory (PNNL)., March 2012. 2011 Building Energy, D&R International, Ltd.
  23. Signh, J.N., Sharma, J.K., 2006. Fuzzy modelling and control of HVAC systems - a review, Journal of scientific and Industrial Research, vol. 65, n°6, pp. 470-476.
  24. Terziyska, M., Todorov, Y., Petrov, M., 2006. Adaptive supervisory tuning of nonlinear model predictive controller for a heat exchanger. Energy saving control in plants and buildings, pp. 93-98.
  25. Wang, S., Xu, X., 2006. Simplified building model for transient thermal performance estimation using GAbased parameter identification, International Journal of Thermal Sciences, vol. 4, n°45, pp. 419-432.
  26. White, J.A., Reichmuth. R., 1996. Simplified method for predicting building energy consumption using average monthly temperatures, In Intersociety Energy Conversion Engineering Conference, pp. 1834-1839.
  27. Williams, B. C., 1989. Temporal qualitative analysis: explaining how physical systems work, In Qualitative reasoning about physical systems, Morgan Kauffmann publishers, pp. 133-177.
  28. Yang, I.H., Yeo, M.S., Kim, K.W., 2003. Application of artificial neural network to predict the optimal start for heating system in building, Energy conversion and Management, vol. 17, n°44, pp. 2791-2809.
  29. Zaheer-uddin, M., Tudoroiu, N., 2004. Neuro-PID tracking control of a discharge air temperature system, Energy Conversion and management, vol. 15-16, n°45, pp. 2405-2415.
Download


Paper Citation


in Harvard Style

Denguir A., Trousset F. and Montmain J. (2014). Aggregated Performance and Qualitative Modeling Based Smart Thermal Control . In Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-039-0, pages 63-76. DOI: 10.5220/0005063300630076


in Bibtex Style

@conference{icinco14,
author={Afef Denguir and Francois Trousset and Jacky Montmain},
title={Aggregated Performance and Qualitative Modeling Based Smart Thermal Control},
booktitle={Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2014},
pages={63-76},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005063300630076},
isbn={978-989-758-039-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Aggregated Performance and Qualitative Modeling Based Smart Thermal Control
SN - 978-989-758-039-0
AU - Denguir A.
AU - Trousset F.
AU - Montmain J.
PY - 2014
SP - 63
EP - 76
DO - 10.5220/0005063300630076