Artificial Neural Networks Based Green Energy Harvesting for Smart World

  • Tigilu Mitiku
  • Mukhdeep Singh ManshahiaEmail author
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 141)


Low-energy technologies in big data applications in remote areas are still unable to offer appropriate level of reliable environment. So it is of utmost importance to meet the energy consumption of Internet of Things (IoT) using renewable energy sources. Wind energy is a vital substitute of traditional fossil fuel due to better efficiency, cost and reliability, and better contribution for sustainable development in future energy generation all over the world. This study presents an artificial neural networks (ANN) based model of power output of wind energy harvesting system in relation with affecting factors such as wind velocity, air density, air pressure, temperature, and relative humidity to meet energy requirements of large scale big data applications in remote areas.


Artificial neural networks Wind energy harvesting system Internet of things 



Authors would like to thank the researchers or academicians whose works have been cited in this review paper. Authors are also grateful to Punjabi University Patiala for offering sufficient library and internet facility and ministry of education of Ethiopia for financial support.


  1. 1.
    Senjyu, T., Satoshi, T., Endusa, M., Naomitsu, U., Hiroshi, K., Toshihisa, F., Hideki, F., Hideomi, S.: Wind velocity and rotor positionsensorless maximum power point tracking control for wind generation system. Renew. Energy 31(11), 1764–1775 (2006)CrossRefGoogle Scholar
  2. 2.
    Petković, D.: Estimation of wind farm efficiency by ANFIS strategy. Godisnjak Pedagoskog fakulteta u Vranju 7, 91–105 (2016)CrossRefGoogle Scholar
  3. 3.
    Petkovic, D., Zarko, C., Vlastimir, N.: Adaptive neuro-fuzzy approach for wind turbine power coefficient estimation. Renew. Sustain. Energy Rev. 20, 191–195 (2013)CrossRefGoogle Scholar
  4. 4.
    Tigilu, M., Manshahia, M.S.: Modeling of wind energy harvesting system: A Systematic Review. Int. J. Eng., Sci. Math. 7(4), 444–467 (2018)Google Scholar
  5. 5.
    Kalogirou, S.A.: Artificial neural networks in energy applications in buildings. Int. J. Low-Carbon Technologies. 1(3), 201–216 (2006)CrossRefGoogle Scholar
  6. 6.
    Mabel, M.C., Fernandez, E.: Analysis of wind power generation and prediction using ANN: A case study. Renew. Energy 33(5), 986–992 (2008)CrossRefGoogle Scholar
  7. 7.
    Rasit, A.T.A., Numan, S.C.: Neural prediction of power factor in wind turbines. IU-J. Electr. Electron. Eng. 7(2), 431–438 (2007)Google Scholar
  8. 8.
    Lei, M., Luan, S., Jiang, C., Liu, H., Zhang, Y.: A review on the forecasting of wind speed and generated power. Renew. Sustain. Energy Rev. 13(4), 915–920 (2009)CrossRefGoogle Scholar
  9. 9.
    Chang, W.Y.: Application of back propagation neural network for wind power generation forecasting. Int. J. Digit. Content Technol. Its Appl. 7(4), 502–509 (2013)CrossRefGoogle Scholar
  10. 10.
    More, A., Deo, M.C.: Forecasting wind with neural networks. Mar. Struct. 16, 35–49 (2003)CrossRefGoogle Scholar
  11. 11.
    Chang, W.Y.: Wind energy conversion system power forecasting using radial basis function neural network. Appl. Mech. Mater., Trans Tech Publ. 284, 1067–1071 (2013)CrossRefGoogle Scholar
  12. 12.
    Tigilu, M., Manshahia, M.S.: Neuro Fuzzy Inference Approach: A Survey. Int. J. Sci. Res. Sci., Eng. Technol. 4(7), 505–519 (2018)Google Scholar
  13. 13.
    Dalibor, P., Shahaboddin, S.: Soft methodology selection of wind turbine parameters to large affect wind energy conversion. Int. J. Electr. Power Energy Syst. 69, 98–103 (2015)CrossRefGoogle Scholar
  14. 14.
    Aamer, B.A., Xiaodong, L.: Estimation of wind turbine power coefficient by adaptive neuro-fuzzy methodology. Neurocomputing 238(C), 227–233 (2017)CrossRefGoogle Scholar
  15. 15.
    Naba, A., Ahmad, N., Takashi H.: Optimal control of variable-speed wind energy conversion system based on fuzzy model power curve. Int. J. Electr. Comput. Sci. 12(2) 2012Google Scholar
  16. 16.
    Marimuthu, C., Kirubakaran, V.: A critical review of factors affecting wind turbine and solar cell system power production. Int. J. Adv. Eng. Res. Stud. 3(2), 143–147 (2014)Google Scholar
  17. 17.
    Rodriguez, C.P., George, J.A.: Energy price forecasting in the Ontario competitive power system market. IEEE Trans. Power Syst. 19(1), 366–374 (2004)CrossRefGoogle Scholar
  18. 18.
    Ajith, A.: Artificial Neural Networks, Handbook of Measuring System Design. Syden-ham, P., Thorn, R. (eds.), pp. 901–908. Wiley, London (2005)Google Scholar
  19. 19.
    Diriba, K.G., Manshahia, M.S.: Nature inspired computational intelligence: a survey. Int. J. Eng. Sci. Math. 6(7), 769–795 (2017)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of MathematicsBule Hora UniversityBule HoraEthiopia
  2. 2.Department of MathematicsPunjabi UniversityPatialaIndia

Personalised recommendations