Advertisement

RNN Based Solar Radiation Forecasting Using Adaptive Learning Rate

  • Ajay Pratap Yadav
  • Avanish Kumar
  • Laxmidhar Behera
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8298)

Abstract

The estimation of solar irradiation data is very important for renewable energy and solar energy systems applications. The forecasts can be used to predict the output power of photovoltaic systems installed in power systems and control the output of other generators to meet the electricity demand. In this paper, a Recurrent Neural Network(RNN) model is used to forecast the Daily, Mean Monthly and Hourly Solar Irradiations using the recorded meteorological data. Here, an adaptive learning rate is proposed for the RNN. The results of the RNN is compared with that of a Multi Layer perceptron(MLP). It is found that the RNN with the adaptive learning rate gives a better performance than the conventional feed forward network.

Keywords

Adaptive Learning Rate Recurrent Neural network(RNN) Solar Radiation forecasting Time Series Prediction 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    International Energy Agency, http://www.iea.org/topics/solarpvandcsp/
  2. 2.
    Hsieh, C.Y., Lu, C.W., Chiang, C.T.: A Recurrent S CMAC GBF based estimation for global solar radiation from environmental information. In: Neural Networks (IJCNN), pp. 1–5 (2010)Google Scholar
  3. 3.
  4. 4.
    Rani, B.I., Rao, D.V.S.K., Ilango, G.S.: Estimation of daily global solar radiation using temperature, relative humidity and seasons with ANN for indian stations. In: Power, Signals, Controls and Computation (EPSCICON), vol. 1, pp. 1–6 (2012)Google Scholar
  5. 5.
    Musellia, M., Paolia, C., Voyanta, C., Niveta, M.L.: Forecasting of prepro-cessed daily solar radiation time series using neural networks. Solar Energy 84(12), 2146–2160 (2010)CrossRefGoogle Scholar
  6. 6.
    Liu, C., Deng, F., Su, G., Wang, Z.: Global solar radiation modeling using the artificial neural network technique. In: Asia-Pacific Power and Energy Engineering Conference (APPEEC), pp. 1–5 (2010)Google Scholar
  7. 7.
    Menghanem, M., Mellit, A., Bendekhis, M.: Artificial neural network model for prediction solar radiation data: application for sizing stand-alone photovoltaic power system. In: Power Engineering Society General Meeting, vol, pp. 40–44. IEEE (2005)Google Scholar
  8. 8.
    Behera, L., Kumar, S., Patnaik, A.: On adaptive learning rate that guarantees convergence in feedforward networks. IEEE Transactions on Neural Networks 17(5), 1116–1125 (2006)CrossRefGoogle Scholar
  9. 9.
    Behera, L., Kar, I.: Intelligent Systems and control principles and applications. Oxford University Press (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Ajay Pratap Yadav
    • 1
  • Avanish Kumar
    • 1
  • Laxmidhar Behera
    • 1
  1. 1.Department of Electrical EngineeringIndian Institute of TechnologyKanpurIndia

Personalised recommendations