Skip to main content
SpringerLink
Log in

Sensitivity analysis of climatic parameters for sky classification

  • Original Paper
  • Published:
Theoretical and Applied Climatology Aims and scope Submit manuscript

Abstract

Climatic variables are frequently used as weighting factors to indicate the degree of clearness for interpreting sky patterns. However, such important parameters are not always widely available and their criteria to define a sky condition are not clear-cut. In addition, certain variables may be more effective than the others in terms of sky identification. This paper studies the capability of various daylight parameters, namely zenith luminance, global, direct-beam and sky-diffuse illuminance, and solar altitude for categorizing the 15 International Commission on Illumination (CIE) standard skies. A new form of artificial neural networks called probabilistic neural network (PNN) which is a powerful technique for pattern recognition was used for the analysis. The findings suggested that the PNN is an appropriate tool when a number of climatic parameters of various criteria for differentiating sky standards are employed, and the ratio of zenith luminance to diffuse illuminance (L z/D v) and solar altitude (α s) are respectively the most and the least significant input parameters for discriminating between the 15 CIE skies.

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
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  • Assunção HF, Escobedo JF, Oliveira AP (2007) A new algorithm to estimate sky condition based on 5 minutes-averaged values of clearness index and relative optical air mass. Theor Appl Climatol 90(3–4):235–248

    Article  ADS  Google Scholar 

  • Bartzokas A, Darula S, Kambezidis HD, Kittler R (2003) Sky luminance distribution in Central Europe and the Mediterranean area during the winter period. J Atmos Sol Terr Phys 65(1):113–119

    Article  ADS  Google Scholar 

  • Bartzokas A, Kambezidis HD, Darula S, Kittler R (2005) Comparison between winter and summer sky-luminance distribution in Central Europe and in the Eastern Mediterranean. J Atmos Sol Terr Phys 67(7):709–718

    Article  ADS  Google Scholar 

  • Basheer IA, Hajmeer MN (2000) Artificial neural networks: fundamentals, computation, design and application. J Microbiol Methods 43(1):3–31

    Article  PubMed  CAS  Google Scholar 

  • Cacoullos T (1966) Estimation of multivariate density. Ann Inst Stat Math 18(2):179–189

    Article  MathSciNet  MATH  Google Scholar 

  • Chen WC, Hsu SW (2007) A neural network approach for an automatic LED inspection system. Expert Syst Appl 33(2):531–537

    Article  Google Scholar 

  • Chirarattananon S, Chaiwiwatworakul P (2007) Distributions of sky luminance and radiance of North Bangkok under standard distributions. Renewable Energy 32(8):1328–1345

    Article  Google Scholar 

  • CIE (1994) Guide to recommended practice of daylight measurement. CIE 109. CIE, Vienna

    Google Scholar 

  • Darula S, Kittler R (2005) New trends in daylight theory based on the new ISO/CIE sky standard 3 zenith luminance formula verified by measurement data under cloudless skies. Build Res J 53(1):9–31

    Google Scholar 

  • Enarun D, Littlefair PJ (1995) Luminance models for overcast skies: assessment using measured data. Light Res Technol 27(1):53–58

    Article  Google Scholar 

  • González PA, Zamarreño JM (2005) Prediction of hourly energy consumption in building based on a feedback artificial neural network. Energy Build 37(6):595–601

    Article  Google Scholar 

  • Hajmeer M, Basheer I (2002) A probabilistic neural network approach for modeling and classification of bacterial growth/no-growth data. J Microbiol Meth 51(2):217–226

    Article  CAS  Google Scholar 

  • ISO (2004) 15469:2004(E)/CIE S 011/E:2003 spatial distribution of daylight—CIE standard general sky. ISO, Geneva

    Google Scholar 

  • Janjai S, Masiri I, Nunez M, Laksanaboonsong J (2008) Modeling sky luminance using satellite data to classify sky conditions. Build Environ 43(12):2059–2073

    Article  Google Scholar 

  • Kasten F, Young AT (1989) Revised optical air mass tables and approximation formula. Appl Opt 28(22):4735–4738

    Article  ADS  PubMed  CAS  Google Scholar 

  • Kim D, Kim DH, Chang S (2008) Application of probabilistic neural network to design breakwater armor blocks. Ocean Eng 35(3–4):294–300

    Article  Google Scholar 

  • Kittler R, Darula S (2002) Parametric definition of the daylight climate. Renewable Energy 26(2):177–187

    Article  Google Scholar 

  • Kittler R, Perez R, Darula S (1997) Sky classification respecting energy-efficient lighting, glare and control needs. J Illum Eng Soc 26(1):57–68

    Google Scholar 

  • Kittler R, Darula S, Perez R (1998) A set of standard skies. Polygrafia, Bratislava

    Google Scholar 

  • Lam JC, Li DHW (1996) Daylight availability in Hong Kong and energy implications. Int J Ambient Energy 17(2):79–88

    Article  Google Scholar 

  • Lebaron BA, Michalsky JJ, Perez R (1990) A simple procedure for correcting shadowband data for all sky conditions. Sol Energy 44(5):249–256

    Article  Google Scholar 

  • Lee EWM, Yuen RKK, Lo SM, Lam KC, Yeoh GH (2004) A novel artificial neural network fire model for prediction of thermal interface location in single compartment fire. Fire Saf J 39(1):67–87

    Article  Google Scholar 

  • Li DHW, Lam JC (2001) An analysis of climatic parameters and sky condition classification. Build Environ 36(4):435–445

    Article  Google Scholar 

  • Li DHW, Lam JC (2003) An analysis of all-sky zenith luminance data for Hong Kong. Build Environ 38(5):739–744

    Article  Google Scholar 

  • Li DHW, Lau CCS (2007) An analysis of non-overcast sky luminance models against Hong Kong data. J Sol Energy Eng 129(4):486–493

    Article  Google Scholar 

  • Li DHW, Tang HL (2008) Standard skies classification in Hong Kong. J Atmos Sol Terr Phys 70(8–9):1222–1230

    Article  Google Scholar 

  • Li DHW, Lam JC, Lau CCS (2002) A study of solar radiation daylight illuminance and sky luminance data measurements for Hong Kong. Archit Sci Rev 45(1):21–30

    Google Scholar 

  • Li DHW, Lau CCS, Lam JC (2003) A study of 15 sky luminance patterns against Hong Kong data. Archit Sci Rev 46(1):61–68

    Google Scholar 

  • Li DHW, Lau CCS, Lam JC (2004a) Overcast sky conditions and luminance distribution in Hong Kong. Build Environ 39(1):101–108

    Article  CAS  Google Scholar 

  • Li DHW, Lau CCS, Lam JC (2004b) Standard skies classification using common climatic parameters. J Sol Energy Eng 126(3):957–964

    Article  Google Scholar 

  • Li DHW, Tang HL, Lee EWM, Muneer T (2010) Classification of CIE standard skies using probabilistic neural network. Int J Climatol 30(2):305–315

    Google Scholar 

  • López G, Gueymard CA (2007) Clear-sky solar luminous efficacy determination using artificial neural networks. Sol Energy 81(7):929–939

    Article  Google Scholar 

  • Markou MT, Bartzokas A, Kambezidis HD (2007) Generation of daylight reference years for two European cities with different climate: Athens, Greece and Bratislava, Slovakia. Atmos Res 86(3–4):315–329

    Article  Google Scholar 

  • Mood AM, Graybill FA (1962) Introduction to the theory of statistics. McGraw-Hill, New York

    Google Scholar 

  • Navarro J, Sendra JJ (2008) Determination of the origin of the illumination vector due to vertical windows under Moon-Spencer sky conditions (uniformly overcast). Renewable Energy 33(1):168–172

    Article  Google Scholar 

  • Navvab M, Karayel M, Ne’eman E, Selkovitz S (1984) Analysis of atmospheric turbidity for daylight calculations. Energy Build 6(2–4):293–303

    Article  Google Scholar 

  • Parzen E (1962) On estimation of a probability density function and mode. Ann Math Stat 33(3):1065–1076

    Article  MathSciNet  MATH  Google Scholar 

  • Quteishat A, Lim CP (2008) A modified fuzzy min-max neural network with rule extraction and its application to fault detection and classification. Appl Soft Comput 8(2):985–995

    Article  Google Scholar 

  • Roisin B, Bodart M, Deneyer A, Herdt PD (2008) Lighting energy savings in offices using different control systems and their real consumption. Energy Build 40(4):514–523

    Article  Google Scholar 

  • Rossini EG, Krenzinger A (2007) Maps of sky relative radiance and luminance distributions acquired with a monochromatic CCD camera. Sol Energy 81(11):1323–1332

    Article  Google Scholar 

  • Soler A, Robledo L (2005) Investigation of the overcast skies luminance distribution using 35 sensors fixed on a dome. Energy Convers Manag 46(17):2739–2747

    Article  Google Scholar 

  • Specht D (1990) Probabilistic neural networks. Neural Netw 3(1):109–118

    Article  Google Scholar 

  • Tregenza PR (1987) Subdivision of the sky hemisphere for luminance measurements. Light Res Technol 19(1):13–14

    Article  Google Scholar 

  • Tregenza PR (2004) Analysing sky luminance scans to obtain frequency distributions of CIE standard general skies. Light Res Technol 36(4):271–281

    Article  Google Scholar 

  • Tregenza PR, Sharples S (1995) New daylight algorithm. University of Sheffield, Sheffield

    Google Scholar 

  • Ward System Group (2000) NeuroShell 2—release 4.0. Ward System Group, Frederick

    Google Scholar 

  • Wittkopf SK, Soon LK (2007) Analysing sky luminance scans and predicting frequent sky patterns in Singapore. Light Res Technol 39(1):31–51

    Article  Google Scholar 

  • Younes S, Muneer T (2007) Clear-sky classification procedures and models using a world-wide data-base. Appl Energy 84(6):623–645

    Article  Google Scholar 

Download references

Acknowledgments

The work described in this paper was fully supported by a grant from the City University of Hong Kong (Project no. 7002284), and K. L. Cheung is supported by a City University of Hong Kong studentship.

Author information

Authors and Affiliations

  1. Building Energy Research Group, Department of Building and Construction, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong SAR, China

    D. H. W. Li, H. L. Tang, K. L. Cheung, E. W. M. Lee & C. C. K. Cheng

Authors
  1. D. H. W. Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. H. L. Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. K. L. Cheung
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. E. W. M. Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. C. C. K. Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to D. H. W. Li.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Li, D.H.W., Tang, H.L., Cheung, K.L. et al. Sensitivity analysis of climatic parameters for sky classification. Theor Appl Climatol 105, 297–309 (2011). https://doi.org/10.1007/s00704-010-0392-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00704-010-0392-6

Keywords

Search

Navigation