DOI QR코드

DOI QR Code

Analyses of Power Consumption of the Heat Pump Dryer in the Automobile Drying Process by using the Principal Component Analysis and Multiple Regression

주성분 분석과 다중회귀모형을 사용한 자동차 건조 공정의 히트펌프 건조기 소모 전력 분석

  • Lee, Chang-Yong (Dept. of Industrial and Systems Engineering, Kongju National University) ;
  • Song, Gensoo (Dept. of Industrial and Systems Engineering, Kongju National University) ;
  • Kim, Jinho (Dept. of Industrial and Systems Engineering, Kongju National University)
  • 이창용 (공주대학교 산업시스템공학과) ;
  • 송근수 (공주대학교 산업시스템공학과) ;
  • 김진호 (공주대학교 산업시스템공학과)
  • Received : 2015.01.07
  • Accepted : 2015.03.06
  • Published : 2015.03.31

Abstract

In this paper, we investigate how the power consumption of a heat pump dryer depends on various factors in the drying process by analyzing variables that affect the power consumption. Since there are in general many variables that affect the power consumption, for a feasible analysis, we utilize the principal component analysis to reduce the number of variables (or dimensionality) to two or three. We find that the first component is correlated positively to the entrance temperature of various devices such as compressor, expander, evaporator, and the second, negatively to condenser. We then model the power consumption as a multiple regression with two and/or three transformed variables of the selected principal components. We find that fitted value from the multiple regression explains 80~90% of the observed value of the power consumption. This results can be applied to a more elaborate control of the power consumption in the heat pump dryer.

Keywords

References

  1. Bannister, P., Carrington, G., and Chen, G., Heat Pump Dehumidifier Drying Technology-Status Potential. Proc. of 7th IEA Heat Pump Conference, 2002, Vol. 1, pp. 219-230.
  2. Barlett, M., Test of Significance of Factor Analysis. British Journal of Psychology, 1950, Vol. 3, pp. 77-85.
  3. Guyon, I. and Elisseeff, A., An introduction to variable and feature selection. The Journal of Machine Learning Research, 2003, Vol. 3, pp. 1157-1182.
  4. Hotelling, H., Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 1933, Vol. 24, pp. 417-441, pp. 498-520. https://doi.org/10.1037/h0071325
  5. Hotelling, H., Relations between two sets of variates. Biometrika, 1936, Vol. 27, pp. 321-77.
  6. Kim, S.B., Feature Extraction/Selection in High-Dimensional Spectral Data. In J. Wang (Ed.), Encyclopedia of Data Warehousing and Mining, Second Edition, Hershey, PA : Information Science Reference, 2009.
  7. Lattin, J., Carroll, J., and Green P., Analyzing multivariate data, Thomson Learning, Pacific Grove, 2003.
  8. Lee, K.H., Kim, O.J., and Lee, S.R., Analysis on the Drying Performance with the Flow Rate of Circulation Air in a Heat Pump Dryer. Korean Journal of Air-Conditioning and Refrigeration Engineering, 2009, Vol. 21, No. 1, pp. 1-8.
  9. Mao, K.Z., Identifying critical variables of principal components for unsupervised feature selection, Systems, Man, and Cybernetics, Part B : Cybernetics. IEEE Transactions, 2005, Vol. 35, pp. 339-344. https://doi.org/10.1109/TSMCB.2004.843269
  10. Mujumdar, A.S., Handbook of Industrial Drying, Marcel Dekker. Inc., New York, 1995.
  11. Pearson, K., On Lines and Planes of Closest Fit to Systems of Points in Space. Philosophical Magazine, 1901, Vol. 2, pp. 559-572. https://doi.org/10.1080/14786440109462720
  12. Sohn, B.H. and Kwon, H.S., Performance Prediction on the Application of a Ground-Source Heat Pump System in an Office Building. Korean Journal of Air-Conditioning and Refrigeration Engineering, 2014, Vol. 26, No. 9, pp. 409-415. https://doi.org/10.6110/KJACR.2014.26.9.409
  13. Sohn, B.H., Choi, J.H., and Min, K.C., Heating Performance of Geothermal Heat Pump System Applied in Cold Climate Region. Korean Journal of Air-Conditioning and Refrigeration Engineering, 2015, Vol. 27, No. 1, pp. 031-038. https://doi.org/10.6110/KJACR.2015.27.1.031
  14. Tenenhaus, M., Esposito Vinzi, V., Chatelinc, Y.-M., and Lauro, C., PLS path modeling. Computational Statistics and Data Analysis, 2005, Vol. 48, pp. 159-205. https://doi.org/10.1016/j.csda.2004.03.005
  15. Theil, H., Economic Forecasts and Policy, Holland, Amsterdam : North, 1961.
  16. Warne, R.T. and Larsen, R., Evaluating a proposed modification of the Guttman rule for determining the number of factors in an exploratory factor analysis. Psychological Test and Assessment Modeling, 2014, Vol. 56, pp. 104-123.
  17. Widjaja, D., Varon, C., Dorado, A., Suykens, J.A., and Van Huffel, S., Application of Kernel Principal Component Analysis for Single-Lead-ECG-Derived Respiration. Biomedical Engineering. IEEE Transactions on, 2012, Vol. 59, pp. 1169-1176. https://doi.org/10.1109/TBME.2012.2186448
  18. Wijesinghe, B., Low Temperature Drying of Food Materials Using Energy-Efficient Heat Pump Dryers. CADDET Newsletter, 1997, No. 7, pp. 4-5.

Cited by

  1. IT 보안 서비스 품질의 측정 방법에 관한 연구 : 정량 지표의 사용 가능성 vol.38, pp.4, 2015, https://doi.org/10.11627/jkise.2015.38.4.30
  2. 미국 금리 스프레드를 이용한 한국 금리 스프레드 예측 모델에 관한 연구 : SVR-앙상블(RNN, LSTM, GRU) 모델 기반 vol.43, pp.3, 2015, https://doi.org/10.11627/jkise.2020.43.3.001