Advertisement

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

  • R. K. TomarEmail author
  • Prakhar Duggal
  • N. D. Kaushika
Conference paper
  • 193 Downloads
Part of the Lecture Notes in Civil Engineering book series (LNCE, volume 36)

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.

Keywords

Expert system approach Solar radiation prediction Artificial neural network model 

References

  1. 1.
    Mohandes M, Rehman S, Halawani TO (1998) Estimation of global solar radiation using artificial neural networks. Renew Energy 14(1–4):179–184CrossRefGoogle Scholar
  2. 2.
    Alawi SM, Hinai HA (1998) An ANN-based approach for predicting global radiation in locations with no direct measurement instrumentation. Renew Energy 14:199–204CrossRefGoogle Scholar
  3. 3.
    Mihalakakou G, Santamouris M, Asimakopoulos DN (2000) The total solar radiation time series simulation in Athens, using neural networks. Theoret Appl Climatol 66:185–197CrossRefGoogle Scholar
  4. 4.
    Sfetsos A, Coonick AH (2000) Univariate and multivariate forecasting of hourly solar radiation with artificial intelligence techniques. Sol Energy 68(2):169–178CrossRefGoogle Scholar
  5. 5.
    Dorvlo ASS, Jervase JA, Al-Lawati A (2002) Solar radiation estimation using artificial neural networks. Appl Energy 71:307–319CrossRefGoogle Scholar
  6. 6.
    Reddy KS, Ranjan M (2003) Solar resource estimation using artificial neural networks and comparison with other correlation models. Energy Convers Manage 44:2519–2530CrossRefGoogle Scholar
  7. 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):3033–3052CrossRefGoogle Scholar
  8. 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 78:752–762CrossRefGoogle Scholar
  9. 9.
    Shah A, Kaushik SC, Garg SN (2006) Computation of beam solar radiation at normal incidence using artificial neural network. Renew Energy 31(10):1483–1491CrossRefGoogle Scholar
  10. 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):1105–1111Google Scholar
  11. 11.
    Lam JC, Kevin KW, Yang L (2008) Solar radiation modeling using ANNs for different climates in China. Energy Convers Manage 49(5):1080–1090CrossRefGoogle Scholar
  12. 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):95–103CrossRefGoogle Scholar
  13. 13.
    Fadare D (2009) Modeling of solar energy potential in Nigeria using an artificial neural network model. Appl Energy 86:1410–1422CrossRefGoogle Scholar
  14. 14.
    Shah A, Kaushik SC, Garg SN (2009) Assessment of diffuse solar energy under general sky condition using artificial neural network. Appl Energy 86:554–564CrossRefGoogle Scholar
  15. 15.
    Tang W, Yang K, He J, Qin J (2010) Quality control and estimation of global solar radiation in China. Sol Energy 84(3):466–475CrossRefGoogle Scholar
  16. 16.
    Paoli C, Voyant C, Muselli M, Nivet ML (2010) Forecasting of preprocessed daily solar radiation time series using neural networks. Sol Energy 84:2146–2160CrossRefGoogle Scholar
  17. 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 51:891–904CrossRefGoogle Scholar
  18. 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):25–33Google Scholar
  19. 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 4:063146CrossRefGoogle Scholar
  20. 20.
    Kaushika ND, Tomar RK, Kaushik SC (2014) Artificial neural network model based on Interrelationship of direct, diffuse and global solar radiations. Sol Energy 103:327–342CrossRefGoogle Scholar
  21. 21.
    Hottel HC (1976) A sample model for estimating the transmittance of direct solar radiation through clear atmosphere. Sol Energy 18:129CrossRefGoogle Scholar
  22. 22.
    Duffie JA, Beckman WA (1991) Solar engineering of thermal processes. Wiley, New YorkGoogle Scholar
  23. 23.
    Liu BYH, Jordan RC (1960) The interrelationship and characteristics distribution of direct, diffuse and total solar radiation. Sol Energy 4:1–19CrossRefGoogle Scholar
  24. 24.
    Haykin S (1998) Neural networks: a comprehensive foundation. Macmillan College Publishing Co., New YorkzbMATHGoogle Scholar
  25. 25.
    Zurada JM (1992) Introduction to artificial neural systems. West, St. PaulGoogle Scholar
  26. 26.
    Mani A (1981) Handbook of solar radiation data for India. Allied, New DelhiGoogle Scholar
  27. 27.
    Tyagi AP (2009) Solar radiant energy over India. India Meteorological Department, Ministry of Earth Sciences, New DelhiGoogle Scholar
  28. 28.
    See portal http://eosweb.larc.nasa.gov/sse/ for NASA SSE

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Civil EngineeringAmity UniversityNoidaIndia
  2. 2.Centre for Energy StudiesIndian Institute of Technology, Hauz khasNew DelhiIndia

Personalised recommendations