Journal of the Korean housing association (한국주거학회논문집)

The Korean Housing Association (한국주거학회)
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Development of ANN- and ANFIS-based Control Logics for Heating and Cooling Systems in Residential Buildings and Their Performance Tests

인공지능망과 뉴로퍼지 모델을 이용한 주거건물 냉난방 시스템 조절 로직 및 예비 성능 시험

  • 문진우 (전남대학교 바이오하우징 연구사업단)
  • Received : 2011.03.21
  • Accepted : 2011.04.27
  • Published : 2011.06.25
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Abstract

This study aimed to develop AI- (Artificial Intelligence) based thermal control logics and test their performance for identifying the optimal thermal control method in buildings. For this objective, a conventional Two-Position On/Off logic and two AI-based variable logics, which applied ANN (Artificial Neural Network) and ANFIS (Adaptive Neuro-Fuzzy Inference System), have developed. Performance of each logic was tested in a typical two-story residential building in U.S.A. using the computer simulation incorporating MATLAB and IBPT (International Building Physics Toolbox). In the analysis of the test results, AI-based control logic presented the advanced thermal comfort with stability compared to the conventional logic while they did not show significant energy saving effects. In conclusion, the predictive and adaptive AI-based control logics have a potential to maintain interior air temperature more comfortably, and the findings in this study could be a solid foundation for identifying the optimal thermal control method in buildings.

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References

  1. 고동석, 곽영훈, 허정호 (2010). 도심지역 에너지계획을 위한 인공신경망 기반의 에너지수요예측에 관한 연구. 대한건축학회논문집 계획계, 26(2), 221-230.
  2. 양인호, 김광우 (1997). 공조설비 최적운전을 위한 신경망 모델의 적용에 관한 연구-최적 기동시간 결정을 위한 신경망 모델의 최적화. 대한건축학회논문집, 13(8), 97-108.
  3. Argiriou, A. A., Bellas-Velidis, I., Kummert, M., & Andre, P. (2004). A neural network controller for hydronic heating systems of solar buildings. Neural Networks, 17(3), 427-440. https://doi.org/10.1016/j.neunet.2003.07.001
  4. ASHRAE (1992). Thermal Environmental Conditions for human occupancy (ANSI/ASHRAE Standard 55-1992). Atlanta, GA: American Society of Heating, Refrigerating, and Air-Conditioning Engineers.
  5. ASHRAE (2004). Energy-Efficient Design of Low-Rise Residential Buildings (ANSI/ASHRAE Standard 90.2- 2004). Atlanta, GA: American Society of Heating, Refrigerating, and Air-Conditioning Engineers.
  6. Aydinalp, K. M., & Ugursal, V. I. (2008). Comparison of neural network, conditional demand analysis, and engineering approaches for modelling end-use energy consumption in the residential sector. Applied Energy, 85(4), 271-296. https://doi.org/10.1016/j.apenergy.2006.09.012
  7. Ben-Nakhi, A. E., & Mahmoud, M. A. (2002). Energy conservation in buildings through efficient A/C control using neural networks. Applied Energy, 73(1), 5-23. https://doi.org/10.1016/S0306-2619(02)00027-2
  8. Bradshaw, V. (1993). Building Control Systems. 2nd edition, John Wiley & Sons, Inc. NewYork, Chichester, Brisbane, Toronto, Singapore.
  9. Datta, D., Tassou, S. A., & Marriott, D. (1997). Application of neural networks for the prediction of the energy consumption in a supermarket. In: Proc, of CLIMA 2000 Conf, Brussels (Belgium), 98-107.
  10. 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. https://doi.org/10.1016/j.rser.2008.09.015
  11. DWYER (2008). HU-1142 [On-line]. Available: http://omnicontrols.com/lists/DwyerRh.htm.
  12. Ertunc, H. M., & Hosoz M. (2008). Comparative analysis of an evaporative condenser using artificial neural networkand adaptive neuro-fuzzy inference system. International Journal of Refrigeration, 31(8), 1426-1436. https://doi.org/10.1016/j.ijrefrig.2008.03.007
  13. Global Controls (2005). EE70 Series [On-line]. Available:http://www.global-controls.net/air_velocity.html.
  14. Gonzalez, P. A., & Zamarreno, J. M. (2005). Prediction of hourly energy consumption in buildings based on a feedback artificial neural network. Energy and Buildings, 37(6), 595-601. https://doi.org/10.1016/j.enbuild.2004.09.006
  15. Gouda, M. M., Danaher, S., & Underwood, C. P. (2006). Quasi-adaptive fuzzy heating control of solar buildings. Building and Environment, 41(12), 1881-1891. https://doi.org/10.1016/j.buildenv.2005.07.008
  16. Alasha'ary, H., Moghtaderi, B., Page, A., & Sugo, H. (2009). A neuro-fuzzy model for prediction of the indoor temperature in typical Australian residential buildings. Energy and Buildings, 41(7), 703-710. https://doi.org/10.1016/j.enbuild.2009.02.002
  17. Harper, R. (2003). Inside the Smart Home. Springer-Verlag. London Limited.
  18. Esen, H., Inalli, M., Sengur, A., & Esen, M. (2008). Predicting performance of a ground-source heat pump system using fuzzy weighted pre-processing-based ANFIS. Building and Environment, 43(12), 2178-2187. https://doi.org/10.1016/j.buildenv.2008.01.002
  19. IBPT (2010). International Building Physics Toolbox in Simulink [On-line]. Available: http://www.ibpt.org/.
  20. Jassar, S., Liao, Z., & Zhao, L. (2009). Adaptive neurofuzzy based inferential sensor model for estimating the average air temperature in space heating systems. Building and Environment, 44(8), 1609-1616. https://doi.org/10.1016/j.buildenv.2008.10.002
  21. Kalogirou, S. A., Neocleous, C. C., & Schizas, C. N. (1996). Building Heating Load Estimation Using Artificial Neural Networks. Proceedings of the International Conference "CLIMA 2000", Brussels.
  22. Kalogirou, S. A., & Bojic, M. (2000). Artificial neural networks for the prediction of the energy consumption of a passive solar building. Energy, 25(5), 479-491. https://doi.org/10.1016/S0360-5442(99)00086-9
  23. Karatasou, S., Santamouris, M., & Geros, V. (2006). Modeling and predicting building's energy use with artificial neural networks: Methods and results. Energy and Buildings, 38(8), 949-958. https://doi.org/10.1016/j.enbuild.2005.11.005
  24. Karlsson, J. F., & Moshfegh, B. (2006). Energy demand and indoor climate in a low energy building-changed control strategies and boundary conditions. Energy and Buildings, 38(4), 315-326. https://doi.org/10.1016/j.enbuild.2005.06.013
  25. Ko, I., Jeon, H., Kwon, H., & Huh, J. (2008). Artificial Neural Networks for Use in Cooling Load Prediction of a Large Exhibition Center. 2008 Korea-Jeju Conference on Wellness, Cheju (Korea), November. 2008, 112-115.
  26. Krarti, M. (2003). An Overview of Artificial Intelligence- Based Methods for Building Energy Systems. Journal of Solar Energy Engineering, 25, 331-342.
  27. Lakewood Engineering (2005). 2069 ET Radiant Heater [On-line]. Available: http://www.lakewoodeng.com/.
  28. Lee, J. Y., Yoe, M. S., & Kim, K. W. (2002). Predictive Control of the Radiant Floor Heating System in Apartment Buildings. Journal of Asian Architecture and Building Engineering, 1(1), 105-112. https://doi.org/10.3130/jaabe.1.105
  29. LG (2008). DH30 GoldStar Dehumidifier [On-line]. Available: http://www.dealtime.com/LG-LG-Gold-Star-30-Pint-Dehumidifier-DH30-RB/prices.
  30. MathWorks. (2010). MATLAB 14 [On-line]. Available: http://www.mathworks.com.
  31. MicroDAQ (2008). HOBO U12 Temp/RH/Light/External Data Logger [On-line]. Available: http://www.microdaq.com/occ/u12/u12-012.php.
  32. McArthur, H., & Spalding, D. (2004). Engineering Materials Science: Properties, Uses, Degradation and Remediation. Horwood Publishing.
  33. Moon, J. W., & Kim, J. J. (2009). Application of ANN (Artificial-Neural-Network) in Residential Thermal Control. 11TH International Building Performance Simulation Association Conference, Building Simulation 2009, University of Strathclyde, Glasgow, July 27-30, 2009, 64-71.
  34. Moon, J. W., & Kim, J. J. (2010). ANN-based thermal control models for residential buildings. Building and Environment, 45(7), 1612-1625. https://doi.org/10.1016/j.buildenv.2010.01.009
  35. Morel, N., Bauer, M., El-Khoury, M., & Krauss, J. (2001). NEUROBAT, A predictive and adaptive heating control system using artificial neural networks. International Journal of Solar Energy, 21, 161-201. https://doi.org/10.1080/01425910108914370
  36. National Semiconductor (2010). LM35 Precision Centigrade Temperature Sensors [On-line]. Available: http://222.national.com.
  37. NIQ (2011). EZIOTM [On-line]. Available: http://www.ezio.com/.
  38. OMEGA (2007). HX93A Temp/RH Transmitter [On-line]. Available: http://www.newportus.com/ppt/HX93A.html,
  39. Parsons, K. C. (2003). Human Thermal Environments. 2ndedition, Taylor & Francis, London.
  40. Shin, K. W., & Lee, Y. S. (2003). The study on cooling load forecast of an unit building using neural networks. International Journal of Air-Conditioning and Refrigeration, 11(4), 170-177.
  41. Soyguder, S., & Alli, H. (2009). An expert system for the humidity and temperature control in HVAC systems using ANFIS and optimization with Fuzzy Modelling Approach. Energy and Buildings, 41(8), 814-822. https://doi.org/10.1016/j.enbuild.2009.03.003
  42. Soyguder, S., & Alli, H. (2009). Predicting of fan speed for energy saving HVAC system based on adaptive network based fuzzy inference system. Expert Systems with Applications, 36(4), 8631-8638. https://doi.org/10.1016/j.eswa.2008.10.033
  43. U.S. Census Bureau (2008). American Housing Survey for the United States 2005 [On-line]. Available: http://www/census.gov/hhes/www/housing/ahs/ahs.html.
  44. VENTA SONIC (2009). VS100 Cool and Warm Ultrasonic Humidifier [On-line]. Available: http://www.digitalfotoclub. com/sc/from-froogle.asp?id=964702410&rf=froogle&dfdate=5_18_2009.
  45. Yang, I. H., & Kim, K. W. (2000). Development of Artificial Neural Network Model for the Prediction of Descending Time of Room Air Temperature. International Journal of Air-Conditioning and Refrigeration, 12(11), 1038-1048.
  46. 1038-1048Yang, I. H., Yeo, M. S., & Kim, K. W. (2003). Application of artificial neural network to predict the optimal start time for heating system in building. Energy Conversion and Management, 44(17), 2791-2009. https://doi.org/10.1016/S0196-8904(03)00044-X
  47. Yang, I. H. (2005). A Study on Determining the Optimal Stop Time of a Heating System. International Journal of Air-Conditioning and Refrigeration, 13(1), 22-30.
  48. Yang, J., Rivard, H., & Zmeureanu, R. (2005). On-line building energy prediction using adaptive artificial neural networks. Energy and Buildings. 37(12), 1250-1259. https://doi.org/10.1016/j.enbuild.2005.02.005

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