Skip to main content

An Easy-to-Understand Expert System for the Prediction of Direct, Diffuse and Global Solar Radiations in Indian Region

  • Conference paper
  • First Online:
Advances in Energy and Built Environment

Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 36))

  • 433 Accesses

Abstract

Building is a modifier of indoor climate to provide comfort to its inhabitant. The building walls and roof often have sloppy surfaces, and the thermal performance evaluation of the building requires simultaneous values of direct, diffuse and global solar radiation. Such values are available only for limited locations. Consequently, the thermal evaluation of building in regions of complex climate and design of efficient air-conditioned system has continually suffered a setback. This paper presents an easy-to-understand expert system for the prediction of direct, diffuse and global solar radiation in the Indian region. The approach is based on a multi-frame output model of the artificial neural network analysis. The computational algorithm includes estimation of direct, diffuse and global components of solar radiation through clear sky conditions. The deviations of these estimates from measurements are considered to be due to random weather phenomena characterized by atmospheric clearness indices which are determined by an artificial neural network analysis based on interrelationship of direct, diffuse and global solar radiation. The target values of atmospheric clearness index achieved as a result of ANN analysis are expressed by an explicit polynomial representation which in turn enables the determination of atmospheric clearness index as a function of latitude and longitude of location, time of the day and month of the year. The whole computational scheme is embedded in a graphical user interface which is designed to assist a wide spectrum of users such as energy planner, engineers, architects, scientists and researchers to predict direct, diffuse and global solar radiation.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Mohandes M, Rehman S, Halawani TO (1998) Estimation of global solar radiation using artificial neural networks. Renew Energy 14(1–4):179–184

    Article  Google Scholar 

  2. Alawi SM, Hinai HA (1998) An ANN-based approach for predicting global radiation in locations with no direct measurement instrumentation. Renew Energy 14:199–204

    Article  Google Scholar 

  3. Mihalakakou G, Santamouris M, Asimakopoulos DN (2000) The total solar radiation time series simulation in Athens, using neural networks. Theoret Appl Climatol 66:185–197

    Article  Google Scholar 

  4. Sfetsos A, Coonick AH (2000) Univariate and multivariate forecasting of hourly solar radiation with artificial intelligence techniques. Sol Energy 68(2):169–178

    Article  Google Scholar 

  5. Dorvlo ASS, Jervase JA, Al-Lawati A (2002) Solar radiation estimation using artificial neural networks. Appl Energy 71:307–319

    Article  Google Scholar 

  6. Reddy KS, Ranjan M (2003) Solar resource estimation using artificial neural networks and comparison with other correlation models. Energy Convers Manage 44:2519–2530

    Article  Google Scholar 

  7. Sozen A, Arcaklioglu E, Ozalp M (2004) Estimation of solar potential in Turkey by artificial neural networks using meteorological and geographical data. Energy Convers Manage 45(18–19):3033–3052

    Article  Google Scholar 

  8. Tymvios FS, Jacovides CP, Michaelides SC, Scouteli C (2005) Comparative study of Angstroms and artificial neural networks methodologies in estimating global solar radiation. Sol Energy 78:752–762

    Article  Google Scholar 

  9. Shah A, Kaushik SC, Garg SN (2006) Computation of beam solar radiation at normal incidence using artificial neural network. Renew Energy 31(10):1483–1491

    Article  Google Scholar 

  10. Krishnaiah T, Srinivasa Rao S, Madhumurthy K, Reddy KS (2007) Neural network approach for modelling global solar radiation. J Appl Sci Res 3(10):1105–1111

    Google Scholar 

  11. Lam JC, Kevin KW, Yang L (2008) Solar radiation modeling using ANNs for different climates in China. Energy Convers Manage 49(5):1080–1090

    Article  Google Scholar 

  12. Mishra A, Kaushika ND, Zhang G, Zhou J (2008) Artificial neural network model for the estimation of direct solar radiation in the Indian zone. Int J Sustain Energy 27(3):95–103

    Article  Google Scholar 

  13. Fadare D (2009) Modeling of solar energy potential in Nigeria using an artificial neural network model. Appl Energy 86:1410–1422

    Article  Google Scholar 

  14. Shah A, Kaushik SC, Garg SN (2009) Assessment of diffuse solar energy under general sky condition using artificial neural network. Appl Energy 86:554–564

    Article  Google Scholar 

  15. Tang W, Yang K, He J, Qin J (2010) Quality control and estimation of global solar radiation in China. Sol Energy 84(3):466–475

    Article  Google Scholar 

  16. Paoli C, Voyant C, Muselli M, Nivet ML (2010) Forecasting of preprocessed daily solar radiation time series using neural networks. Sol Energy 84:2146–2160

    Article  Google Scholar 

  17. Sahin M, Kaya Y, Uyar M (2012) Comparison of ANN and MLR models for estimating solar radiation in Turkey using NOAA/ AVHRR data. Adv Space Res 51:891–904

    Article  Google Scholar 

  18. Khatib T, Mohamed A, Mahmoud M, Sopian K (2012) Estimating global solar energy using multilayer perception artificial neural network. Int J Energy 6(1):25–33

    Google Scholar 

  19. Tomar RK, Kaushika ND, Kaushik SC (2012) Artificial neural network based computational model for the prediction of direct solar radiation in Indian zone. J Renew Sustain Energy 4:063146

    Article  Google Scholar 

  20. Kaushika ND, Tomar RK, Kaushik SC (2014) Artificial neural network model based on Interrelationship of direct, diffuse and global solar radiations. Sol Energy 103:327–342

    Article  Google Scholar 

  21. Hottel HC (1976) A sample model for estimating the transmittance of direct solar radiation through clear atmosphere. Sol Energy 18:129

    Article  Google Scholar 

  22. Duffie JA, Beckman WA (1991) Solar engineering of thermal processes. Wiley, New York

    Google Scholar 

  23. Liu BYH, Jordan RC (1960) The interrelationship and characteristics distribution of direct, diffuse and total solar radiation. Sol Energy 4:1–19

    Article  Google Scholar 

  24. Haykin S (1998) Neural networks: a comprehensive foundation. Macmillan College Publishing Co., New York

    MATH  Google Scholar 

  25. Zurada JM (1992) Introduction to artificial neural systems. West, St. Paul

    Google Scholar 

  26. Mani A (1981) Handbook of solar radiation data for India. Allied, New Delhi

    Google Scholar 

  27. Tyagi AP (2009) Solar radiant energy over India. India Meteorological Department, Ministry of Earth Sciences, New Delhi

    Google Scholar 

  28. See portal http://eosweb.larc.nasa.gov/sse/ for NASA SSE

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to R. K. Tomar .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Tomar, R.K., Duggal, P., Kaushika, N.D. (2020). An Easy-to-Understand Expert System for the Prediction of Direct, Diffuse and Global Solar Radiations in Indian Region. In: Zhang, G., Kaushika, N., Kaushik, S., Tomar, R. (eds) Advances in Energy and Built Environment. Lecture Notes in Civil Engineering , vol 36. Springer, Singapore. https://doi.org/10.1007/978-981-13-7557-6_22

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-7557-6_22

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-7556-9

  • Online ISBN: 978-981-13-7557-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics