Skip to main content
SpringerLink
Log in
Book cover

International Conference on Next Generation Computing Technologies

NGCT 2017: Smart and Innovative Trends in Next Generation Computing Technologies pp 349–362Cite as

Forecasting Hydrogen Fuel Requirement for Highly Populated Countries Using NARnet

  • Conference paper
  • First Online:

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 827))

Abstract

Petroleum is being consumed at a rapid pace all over world, but the amount of petroleum is constant in earth crust and production to consumption requirement is not up to mark. It is expected that a day may come when this world will witness the crisis of this oil. For this our paper addresses the prediction of petroleum crisis in two most populated country of the world i.e. India and China using novel Artificial Neural Network (ANN) based approach. The set of observation comprising three features like population, petroleum production and petroleum consumption are being considered to design the predictive model. Our work shows that petroleum production over consumption with respect to sharp increase of population, leads to a decisive issue in production of an alternative fuel like Hydrogen fuel. In our analysis, we used the data provided by different government sources over a period of more than 30 years and then simulated by a multistep ahead prediction methodology, i.e. nonlinear autoregressive Network (NARnet) to predict petroleum crisis in near future. The results of present study reveals that for India, the Normalized Mean Square Error (NMSE) values found for population petroleum production and consumption are 0.000046, 0.2233 and 0.0041 respectively. Similarly for China the corresponding values are 0.0011, 0.0126 and 0.0041 respectively, which validates the accuracy of the proposed model. The study forecasts that by 2050 hydrogen fuel may be a suitable replacement for petroleum, and will not only reduce pollution, but also enhance the fuel efficiency at lower cost as compared to that of petroleum.

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   99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   129.99
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

Notes

  1. 1.

    Population of India and China. Available at: www.data.worldbank.org/country/india & www.data.worldbank.org/country/china.

  2. 2.

    India Crude Oil Production and Consumption by Year, Source: United States Energy InformationAdministration. Available at: www.indexmundi.com/energy.aspx?country=in.

  3. 3.

    China Crude Oil Production and Consumption by Year, Source: United States Energy Information Administration. Available at: www.indexmundi.com/energy.aspx?country=cn&product=oil&graph=production+consumption.

  4. 4.

    Obitko, M.: Prediction using Neural networks (1999). http://www.obitko.com/tutorials/neural-network-prediction.

  5. 5.

    http://www.mpipks-dresden.mpg.de/~tisean/TISEAN_2.1/docs/chaospaper/node20.html.

  6. 6.

    https://en.wikipedia.org/wiki/Autoregressive%E2%80%93moving-average_model.

  7. 7.

    www.mpipks-dresden.mpg.de/~tisean/TISEAN_2.1/docs/chaospaper/node32.html.

  8. 8.

    U.S. Energy Information Administration (EIA) reports on India (2014). Available online at: http://www.eia.gov/todayinenergy/detail.cfm?id=17551.

  9. 9.

    U.S. Energy Information Administration (EIA) reports on China(2015). Available at: http://www.connaissancedesenergies.org/sites/default/files/pdf-ptvue/analysis_report_on_china.pdf.

  10. 10.

    source: Authors’ analysis: Ministry of Road Transport and Highways, India. http://www.morth.nic.in/.

  11. 11.

    source: Authors’ analysis: National Bureau Of Statistics of Chaina. http://www.stats.gov.cn/ENGLISH/Statisticaldata/AnnualData/.

  12. 12.

    http://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy/oil-review-by-energy-type/oil-reserves.html.

References

  1. Mitchell, J., Marcel, V., Mitchell, B.: What Next for the Oil and Gas Industry?. Chatham House, London (2012)

    Google Scholar 

  2. Davis, S.C., Diegel, S.W., Boundy, R.G.: Transportation Energy Data Book (2016)

    Google Scholar 

  3. Yuan, C., Liu, S., Fang, Z.: Comparison of China’s primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM (1, 1) model. Energy 100, 384–390 (2016)

    Article  Google Scholar 

  4. Srinivasan, T.N.: China and India: economic performance, competition and cooperation: an update. J. Asian Econ. 15(4), 613–636 (2004)

    Article  MathSciNet  Google Scholar 

  5. Hu, J.W.S., Hu, Y.C., Lin, R.R.W.: Applying neural networks to prices prediction of crude oil futures. Math. Probl. Eng. 2012, 1–13 (2012)

    Google Scholar 

  6. Khazem, H., Mazouz, A.: Forecasting the price of crude oil using artificial neural networks. Int. J. Bus. Mark. Decis. Sci. 6(1), 119–135 (2013)

    Google Scholar 

  7. Bossel, U.: The physics of the hydrogen economy. Eur. Fuel Cell News 10(2), 1–16 (2003)

    Google Scholar 

  8. Pillay, P.: Hydrogen economy and alternative fuels. IEEE Emerg. Technol. Portal 2012 (2006)

    Google Scholar 

  9. Serrano, E., Rus, G., Garcia-Martinez, J.: Nanotechnology for sustainable energy. Renew. Sustain. Energy Rev. 13(9), 2373–2384 (2009)

    Article  Google Scholar 

  10. Sahaym, U., Norton, M.G.: Advances in the application of nanotechnology in enabling a ‘hydrogen economy’. J. Mater. Sci. 43(16), 5395–5429 (2008)

    Article  Google Scholar 

  11. Wali, A.N., Kagoyire, E., Icyingeneye, P.: Mathematical modelling of Uganda population growth. Appl. Math. Sci. 6(84), 4155–4168 (2012)

    MATH  Google Scholar 

  12. Armstrong, J.S.: Research needs in forecasting. Int. J. Forecast. 4(3), 449–465 (1988)

    Article  Google Scholar 

  13. Zhang, H., Li, J.: Prediction of tourist quantity based on RBF neural network. JCP 7(4), 965–970 (2012)

    Google Scholar 

  14. Keskin, M.E., Taylan, E.D., Yilmaz, G.: Flow prediction model with fuzzy logic approaches: dim stream. In: International River Basin Management Congress (2007). http://www.dsi.gov.tr/english/congress2007/chapter_4/107.pdf

  15. Eftekhari, A., Moghaddam, H.A., Forouzanfar, M., Alirezaie, J.: Incremental local linear fuzzy classifier in fisher space. EURASIP J. Adv. Sig. Process. 2009, 15 (2009)

    MATH  Google Scholar 

  16. Yasdi, R.: Prediction of road traffic using a neural network approach. Neural Comput. Appl. 8(2), 135–142 (1999)

    Article  Google Scholar 

  17. Thenmozhi, M.: Forecasting stock index returns using neural networks. Delhi Bus. Rev. 7(2), 59–69 (2006)

    Google Scholar 

  18. Tang, Z., Leung, C.W., Bagchi, K.: Improving population estimation with neural network models. In: Wang, J., Yi, Z., Zurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3973, pp. 1181–1186. Springer, Heidelberg (2006). https://doi.org/10.1007/11760191_172

    Chapter  Google Scholar 

  19. Zhang, G., Patuwo, B.E., Hu, M.Y.: Forecasting with artificial neural networks: the state of the art. Int. J. Forecast. 14(1), 35–62 (1998)

    Article  Google Scholar 

  20. Azadeh, A., Sheikhalishahi, M., Shahmiri, S.: A hybrid neuro-fuzzy simulation approach for improvement of natural gas price forecasting in industrial sectors with vague indicators. Int. J. Adv. Manuf. Technol. 62(1), 15–33 (2012)

    Article  Google Scholar 

  21. Nazzal, J.M., El-Emary, I.M., Najim, S.A.: Investigating Jordan oil shale properties using artificial neural network (ANN). World Appl. Sci. J. 5, 553–559 (2008)

    Google Scholar 

  22. Jayaraj, S., Padmakumari, K., Sreevalsan, E., Arun, P.: Wind speed and power prediction using artificial neural networks. In: European Wind Energy Conference, November 2004

    Google Scholar 

  23. Kulkar, S., Haidar, I.: Forecasting model for crude oil price using artificial neural networks and commodity future prices. Int. J. Comput. Sci. Inf. Secur. 2(1), 81–88 (2009)

    Google Scholar 

  24. Aksoy, F., Yabanova, I., Bayrakçeken, H.: Estimation of dynamic viscosities of vegetable oils using artificial neural networks. Indian J. Chem. Technol. 18, 227–233 (2011)

    Google Scholar 

  25. Lackes, R., Börgermann, C., Dirkmorfeld, M.: Forecasting the price development of crude oil with artificial neural networks. In: Omatu, S., Rocha, Miguel P., Bravo, J., Fernández, F., Corchado, E., Bustillo, A., Corchado, Juan M. (eds.) IWANN 2009. LNCS, vol. 5518, pp. 248–255. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02481-8_36

    Chapter  Google Scholar 

  26. Liu, J., Tang, Z.H., Zeng, F., Li, Z., Zhou, L.: Artificial neural network models for prediction of cardiovascular autonomic dysfunction in general Chinese population. BMC Med. Inform. Decis. Making 13(1), 80 (2013)

    Article  Google Scholar 

  27. Maliki, O.S., Agbo, A.O., Maliki, A.O., Ibeh, L.M., Agwu, C.O.: Comparison of regression model and artificial neural network model for the prediction of electrical power generated in Nigeria. Adv. Appl. Sci. Res. 2(5), 329–339 (2011)

    Google Scholar 

  28. Tehrani, R., Khodayar, F.: A hybrid optimized artificial intelligent model to forecast crude oil using genetic algorithm. Afr. J. Bus. Manag. 5(34), 13130 (2011)

    Article  Google Scholar 

  29. Yadav, A.K., Chandel, S.S.: Artificial neural network based prediction of solar radiation for Indian stations. Int. J. Comput. Appl. 50(9), 1–4 (2012)

    Google Scholar 

  30. Mohapatra, S.K., Kamilla, S.K., Mohapatra, S.K.: A pathway to hydrogen economy: artificial neural network an approach to prediction of population and number of registered vehicles in India. Adv. Sci. Lett. 22(2), 359–362 (2016)

    Article  Google Scholar 

  31. Cui, X., Jiang, M.: Chaotic time series prediction based on binary particle swarm optimization. AASRI Procedia 1, 377–383 (2012)

    Article  Google Scholar 

  32. Gibson, D., Nur, D.: Threshold autoregressive models in finance: a comparative approach. In: Proceedings of the Fourth Annual ASEARC Conference. University of Western Sydney, Paramatta, Australia (2011). http://ro.uow.edu.au/asearc/26

  33. Hansen, B.E.: Threshold autoregression in economics. Stat. Interface 4(2), 123–127 (2011)

    Article  MathSciNet  Google Scholar 

  34. Chander, P.: The nonlinear input-output model. J. Econ. Theory 30(2), 219–229 (1983)

    Article  MathSciNet  Google Scholar 

  35. Markopoulos, A.P., Georgiopoulos, S., Manolakos, D.E.: On the use of back propagation and radial basis function neural networks in surface roughness prediction. J. Ind. Eng. Int. 12, 389–400 (2016)

    Article  Google Scholar 

  36. Poli, A.A., Cirillo, M.C.: On the use of the normalized mean square error in evaluating dispersion model performance. Atmos. Environ. Part A. Gen. Top. 27(15), 2427–2434 (1993)

    Article  Google Scholar 

  37. Tverberg, G.: Oil limits and the end of the debt super-cycle (2016). https://ourfiniteworld.com/2016/01/07/2016-oil-limits-and-the-end-of-the-debt-supercycle

  38. Streifel, S.: Impact of China and India on global commodity markets: focus on metals and minerals and petroleum. Development Prospects Group/World Bank, UU World Investment Report (2006)

    Google Scholar 

  39. Offer, G.J., Howey, D., Contestabile, M., Clague, R., Brandon, N.P.: Comparative analysis of battery electric, hydrogen fuel cell and hybrid vehicles in a future sustainable road transport system. Energy Policy 38(1), 24–29 (2010)

    Article  Google Scholar 

  40. Cheng, X., Shi, Z., Glass, N., Zhang, L., Zhang, J., Song, D., Liu, L.S., Wang, H., Shen, J.: A review of PEM hydrogen fuel cell contamination: impacts, mechanisms, and mitigation. J. Power Sources 165(2), 739–756 (2007)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, ITER, Siksha ‘O’ Anusandhan University, Bhubaneswar, 751030, Odisha, India

    Srikanta Kumar Mohapatra

  2. Department of Computer Application, ITER, Siksha ‘O’ Anusandhan University, Bhubaneswar, 751030, Odisha, India

    Tripti Swarnkar

  3. Department of Physics, ITER, Siksha ‘O’ Anusandhan University, Bhubaneswar, 751030, Odisha, India

    Sushanta Kumar Kamilla

  4. Apex Resources Inc., 549 Stonegate Dr., Katy, TX, 77494, USA

    Susanta Kumar Mohapatra

Authors
  1. Srikanta Kumar Mohapatra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tripti Swarnkar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sushanta Kumar Kamilla
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Susanta Kumar Mohapatra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sushanta Kumar Kamilla .

Editor information

Editors and Affiliations

  1. Indian Institute of Technology Patna, Patna, Bihar, India

    Pushpak Bhattacharyya

  2. University of Petroleum and Energy Studies, Dehradun, India

    Hanumat G. Sastry

  3. University of Petroleum and Energy Studies, Dehradun, India

    Venkatadri Marriboyina

  4. University of Petroleum and Energy Studies, Dehradun, India

    Rashmi Sharma

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mohapatra, S.K., Swarnkar, T., Kamilla, S.K., Mohapatra, S.K. (2018). Forecasting Hydrogen Fuel Requirement for Highly Populated Countries Using NARnet. In: Bhattacharyya, P., Sastry, H., Marriboyina, V., Sharma, R. (eds) Smart and Innovative Trends in Next Generation Computing Technologies. NGCT 2017. Communications in Computer and Information Science, vol 827. Springer, Singapore. https://doi.org/10.1007/978-981-10-8657-1_27

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-8657-1_27

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8656-4

  • Online ISBN: 978-981-10-8657-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation