Skip to main content

Advertisement

SpringerLink
Log in

Effective and Comfortable Power Control Model Using Kalman Filter for Building Energy Management

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

In building environment energy management is a big problem in recent years. Several methods and proposals exist in the literature for energy management, but the trade-off between occupants comfort level and energy usage is still a major challenge and remained unresolved. In this paper, we propose power control model for comfortable and energy saving using fuzzy controller and Kalman filter. We have given focus in two directions simultaneously: first is to maximize the occupants comfort level and second is to control the usage of the power. To achieve these tasks, first we implement fuzzy logic to control the environment and second, we predict the consume power using Kalman filter. The parameters we consider are temperature, illumination and air quality. At the end of the paper we compare the power consumption results in case of prediction and with no prediction. The results proved the effectiveness of the proposed technique in obtaining the solution for the aforementioned problems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Owen, M. S. (2009). ASHRAE handbook—fundamentals (I-P edition). American Society of Heating, Refrigerating and Air-Conditioning Engineers.

  2. Wang, Z., Yang, R., & Wang, L. (2010). Multi-agent control system with intelligent optimization for smart and energy-efficient buildings. In Proceedings of the 36th annual conference of the IEEE Industrial Electronics Society, pp. 1144–1149, November 2010.

  3. Dounis, A. I., & Caraiscos, C. (2009). Advanced control systems engineering for energy and comfort management in a building environment—a review. Renewable and Sustainable Energy Reviews, 13(6–7), 1246–1261.

    Article  Google Scholar 

  4. Wang, Z., Yang, R., & Wang, L. (2010). Multi-agent intelligent controller design for smart and sustainable buildings. In Proceedings of 4th annual IEEE international systems conference, pp. 277–282.

  5. Emmerich, S. J., & Persily, A. K. (2001). State-of-the-art review of CO2 demand controlled ventilation technology and application. National Institute of Standards and Technology, Technology Administration, US. Department of Commerce, March 2001.

  6. Bernard, C., Guerrier, B., & Rasset-Louerant, M. M. (1982). Optimal building energy management. Part II: Control. ASME Journal of Solar Energy Engineering, 114, 13–22.

    Article  Google Scholar 

  7. Curtis, P. S., Shavit, G., & Kreider, K. (1996). Neural networks applied to buildings—a tutorial and case studies in prediction and adaptive control. ASHRAE Transactions, 102(1), 732–737.

    Google Scholar 

  8. Wright, J. A., & Handby, V. I. (1987). The formulation, characteristics and solution of HVAC system optimized design problems. ASHRAE Transactions, 93(2), 2133–2145.

    Google Scholar 

  9. Wang, Z., Yang, R., & Wang, L. (2011). Intelligent multi-agent control for integrated building and micro-grid systems. In Proceedings of the innovative smart grid technologies (ISGT): IEEE PES, pp. 1–7, January 2011.

  10. Huang, W., & Lam, H. N. (1997). Using genetic algorithms to optimize controller parameters for HVAC systems. Energy and Buildings, 26(3), 277–282.

    Article  Google Scholar 

  11. Obara, S., & Kudo, K. (2003). Multiple-purpose operational planning of fuel cell and heat pump compound system using genetic algorithm. Transaction of the Society of Heating, Air-Conditioning and Sanitary Engineers of Japan, 9(1), 65–75.

    Google Scholar 

  12. Wright, J. I., Loosemore, H. A., & Farmani, R. (2002). Optimization of building thermal design and control by multi-criterion genetic algorithm. Energy and Buildings, 34(9), 959–972.

    Article  Google Scholar 

  13. Hongwei, Li, Nalimi, R., & Haldi, P. A. (2006). Thermal-economic optimization of a distributed multi-generation energy system. A case study of Beijing. Applied Thermal Engineering, 26(7), 709–719.

    Article  Google Scholar 

  14. Zhen-Ya, Z., Hong-Mei, C., & Shu-Guang, Z. (2010). An approach to the identification of temperature in intelligent building based on feed forward neural network and genetic algorithm. In Proceedings of 3rd IEEE international conference computer science and information technology (ICCSIT), pp. 286–290, July 2010.

  15. Yu, T. (2010). Modeling occupancy behavior for energy efficiency and occupants comfort management in intelligent buildings. In Proceedings of ninth international conference on machine learning and applications (ICMLA), pp. 726–731.

  16. Ming-hai, L., & Qing-chang, R. (2010). Optimization for the chilled water system of HVAC systems in an intelligent building. In Proceedings of international conference on computational and information sciences (ICCIS), pp. 889–891, December 2010.

  17. Fong, K. F., Hanby, V. I., & Chow, T. T. (2006). HVAC system optimization for energy management by evolutionary programming. Energy and Buildings, 38(3), 220–231.

    Article  Google Scholar 

  18. Montazeri-Gh, M., Poursamad, A., & Ghalichi, B. (2006). Application of genetic algorithm for optimization of control strategy in parallel hybrid electric vehicles. Journal of the Franklin Institute, 343(4–5), 420–435.

    Article  MATH  Google Scholar 

  19. Azadeh, A., & Tarverdian, S. (2007). Integration of genetic algorithm, computer simulation and design of experiments for forecasting electrical energy consumption. Energy Policy, 35(10), 5229–5241.

    Article  Google Scholar 

  20. Fong, K. F., Hanby, V. I., & Chow, T. T. (2009). System optimization for HVAC energy management using the robust evolutionary algorithm. Applied Thermal Engineering, 29(11–12), 2327–2334.

    Article  Google Scholar 

  21. Mossolly, M., Ghali, K., & Ghaddar, N. (2009). Optimal control strategy for a multi-zone air conditioning system using a genetic algorithm. Energy, 34(1), 58–66.

    Article  Google Scholar 

  22. Sorrentino, M., Arsie, I., Di-Martino, R., & Rizzo, G. (2010). On the use of genetic algorithm to optimize the on-board energy management of a hybrid solar vehicle. In Proceedings of IFP international conference—advances in hybrid powertrains, pp. 133–143, January–February 2010.

  23. Kayo, G., & Ooka, R. (2009). Application multi-objective genetic algorithm for optimal design method of distributed energy system. In Proceedings of eleventh international IBPSA conference, pp. 167–172, July 2009.

  24. Li, M., Wang, F., & Wang, D. (2011). Preheating simulation of temperature rising of power battery. Journal of Convergence, 2(2), 9–12.

    Google Scholar 

  25. Ling, A. P. A., & Masao, M. (2011). Selection of model in developing information security criteria for smart grid security system. Journal of Convergence, 2(1), 39–46.

    Google Scholar 

  26. Silas, S., Ezra, K., & Rajsing, E. B. (2012). A novel fault tolerant service selection framework for pervasive computing. Human-centric Computing and Information Sciences, 2(5), 1–14.

    Google Scholar 

  27. Liang, W. Y., Lai, P. T., & Chiou, C. W. (2010). An energy conservation DVFS algorithm for Android operating system. Journal of Convergence, 1(1), 93–100.

    Google Scholar 

  28. Zadeh, L. A. (1968). Fuzzy algorithms. Information and Control, 12(2), 94–102.

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgments

This research was supported by the ICT Standardization program of MISP (The Ministry of Science, ICT & Future Planning). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2011-0015009).

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Jeju National University, Jeju City, Republic of South Korea

    Safdar Ali & Do-Hyeun Kim

Authors
  1. Safdar Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Do-Hyeun Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Safdar Ali.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ali, S., Kim, DH. Effective and Comfortable Power Control Model Using Kalman Filter for Building Energy Management. Wireless Pers Commun 73, 1439–1453 (2013). https://doi.org/10.1007/s11277-013-1259-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-013-1259-9

Keywords

Search

Navigation