Skip to main content
SpringerLink
Log in
Book cover

International Conference on Computational Collective Intelligence

ICCCI 2016: Computational Collective Intelligence pp 230–238Cite as

Global Solar Radiation Prediction Using Backward Propagation Artificial Neural Network for the City of Addis Ababa, Ethiopia

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9875))

Abstract

Ethiopia is located close to the equatorial belt that receives abundant solar energy. For Ethiopia, to achieve the optimum utilization of solar energy, it is necessary to evaluate the incident solar radiation over the countries of interest. Though, sophisticated and costly equipment are available but they are very limited for developing countries’ like Ethiopia. This paper is therefore tries to explore the use of artificial neural network method for predicting the daily global solar radiation in the horizontal surface using secondary data in the city of Addis Ababa. For this purpose, the meteorological data of 1195 days from one station in Addis Ababa along the years 1985–1987 were used for training testing and validating the model All independent variables (Min and Max Temperature, humidity, sunshine hour and wind speed were normalized and added to the model. Then, Back propagation (BP) Artificial Neural Network (ANN) method was applied for prediction and training respectively to determine the most suitable independent (input) variables. The results obtained by the ANN model were validated with the actual data and error values were found within acceptable limits. The findings of the study show that the Root Mean Square Error (RMSE) is found to be 0.11 and correlation coefficient (R) value was obtained 0.901 during prediction.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Learn about institutional subscriptions

References

  1. Mekonnen, S.A.: Solar energy assessment in Ethiopia: modeling and measurement. Addis Ababa University Department of Environmental Science, July 2007

    Google Scholar 

  2. Kassahun, G.S.: Predictive modelling of Kaliti wastewater treatment plant performance using Artificial Neural Networks, Chemical Engineering with Specialization in Environmental Engineering, February 2012

    Google Scholar 

  3. Ethio Resource Group with Partners, Solar and Wind Energy Utilization and Project Development Scenarios, Ethiopian Rural Energy Development and Promotion Center, October 2007

    Google Scholar 

  4. Azadeh, A., Maghsoudi, A., Sohrabkhani, S.: An integrated artificial neural networks approach for predicting global radiation. Energy Convers. Manage. 50(6), 1497–1505 (2009)

    Article  Google Scholar 

  5. Ali, R.: Estimating global solar radiation using artificial neural network and air temperature data in a semi-arid environment. Renew. Energy 35(9), 2131–2135 (2010)

    Article  Google Scholar 

  6. Benghanem, M., Mellit, A., Alarm, S.N.: ANN-based modelling and estimation of daily global solar radiation data: a case study. Energy Convers. Manage. 50(7), 1644–1655 (2009)

    Article  Google Scholar 

  7. Cao, J.C., Cao, S.H.: Study of forecasting solar irradiance using neural networks with preprocessing sample data by wavelet analysis. Energy 31(15), 3435–3445 (2006)

    Article  Google Scholar 

  8. Chang, F.-J., Kao, L., Kuo, Y.-M., et al.: Artificial neural networks for estimating regional arsenic concentrations in a black foot disease area in Taiwan. J. Hydrol. 388, 65–76 (2010)

    Article  Google Scholar 

  9. Jiang, Y.: Computation of monthly mean daily global solar radiation in China using artificial neural networks and comparison with other empirical models. Energy 34(9), 1276–1283 (2009)

    Article  Google Scholar 

  10. Singh, K.P., Gupta, S.: Artificial intelligence based modeling for predicting the disinfection by-products in water. Chemom. Intell. Lab. Syst. 114, 122–131 (2012)

    Article  Google Scholar 

  11. Vakili, M., Sabbagh-Yazdi, S.-R., Kalhor, K., Khosrojerdi, S.: Using Artificial Neural Networks for prediction of global solar radiation in Tehran considering particulate matter air pollution. Energy Procedia 74, 1205–1212 (2015)

    Article  Google Scholar 

  12. Olatomiwa, L., Mekhilef, S., Shamshirband, S., Petković, D.: Adaptive neuro-fuzzy approach for solar radiation prediction in Nigeria. Renew. Sustain. Energy Rev. 51, 1784–1791 (2015)

    Article  Google Scholar 

  13. Jiang, H., Dong, Y., Wang, J., Li, Y.: Intelligent optimization models based on hard-ridge penalty and RBF for forecasting global solar radiation. Energy Convers. Manage. 95(1), 42–58 (2015)

    Article  Google Scholar 

  14. Mohammadi, K., Shamshirband, S., Petković, D., Khorasanizadeh, H.: Determining the most important variables for diffuse solar radiation prediction using adaptive neuro-fuzzy methodology; case study: City of Kerman. Iran, Renew. Sustain. Energy Rev. 53, 1570–1579 (2016)

    Article  Google Scholar 

  15. Akarslan, E., Hocaoglu, F.O.: A novel adaptive approach for hourly solar radiation forecasting. Renew. Energy 87(1), 628–633 (2016)

    Article  Google Scholar 

  16. Kumar, R., Aggarwal, R.K., Sharma, J.D.: Comparison of regression and artificial neural network models for estimation of global solar radiations. Renew. Sustain. Energy Rev. 52, 1294–1299 (2015)

    Article  Google Scholar 

  17. Çelik, Ö., Teke, A., Yıldırım, H.B.: The optimized artificial neural network model with Levenberg–Marquardt algorithm for global solar radiation estimation in Eastern Mediterranean Region of Turkey. J. Cleaner Prod. 116(10), 1–12 (2016)

    Article  Google Scholar 

  18. Eriko, I., Kenji, O., Yoichi, M.I., Makoto, A.: A neural network approach to simple prediction of soil nitrification potential: a case study in Japanese temperate forests. Ecol. Model. 219(1–2), 200–211 (2008)

    Google Scholar 

  19. Hu, J., Zhou, G., Xu, X.: Using an improved back propagation neural network to study spatial distribution of sunshine illumination from sensor network data. Ecol. Model. 26, 86–96 (2013)

    Google Scholar 

  20. Krejcar, O., Mahdal, M.: Optimized solar energy power supply for remote wireless sensors based on IEEE 802.15.4 standard. Int. J. Photoenergy 2012, 9 (2012). doi:10.1155/2012/305102. Article ID: 305102

    Article  Google Scholar 

Download references

Acknowledgment

This work and the contribution were supported by project “Smart Solutions for Ubiquitous Computing Environments” FIM, University of Hradec Kralove, Czech Republic (under ID: UHK-FIM-SP-2016-2102).

Author information

Authors and Affiliations

  1. School of Mechanical and Industrial Engineering, Addis Ababa Institute of Technology, Addis Ababa University, Addis Ababa, Ethiopia

    Younas Worki & Eshetie Berhan

  2. Faculty of Informatics and Management, Center for Basic and Applied Research, University of Hradec Kralove, Rokitanskeho 62, 50003, Hradec Kralove, Czech Republic

    Ondrej Krejcar

Authors
  1. Younas Worki
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eshetie Berhan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ondrej Krejcar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ondrej Krejcar .

Editor information

Editors and Affiliations

  1. Wrocław University of Technology, Wrocław, Poland

    Ngoc-Thanh Nguyen

  2. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Lazaros Iliadis

  3. Department of Forestry and Management, Democritus University of Thrace, Orestiada, Thrace, Greece

    Yannis Manolopoulos

  4. Wrocław University of Technology, Wrocław, Poland

    Bogdan Trawiński

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Worki, Y., Berhan, E., Krejcar, O. (2016). Global Solar Radiation Prediction Using Backward Propagation Artificial Neural Network for the City of Addis Ababa, Ethiopia. In: Nguyen, NT., Iliadis, L., Manolopoulos, Y., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2016. Lecture Notes in Computer Science(), vol 9875. Springer, Cham. https://doi.org/10.1007/978-3-319-45243-2_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-45243-2_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45242-5

  • Online ISBN: 978-3-319-45243-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation