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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
International Energy Agency, http://www.iea.org/topics/solarpvandcsp/
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)
Annual Energy Outlook 2013, http://www.eia.gov/forecasts/aeo/pdf/0383.pdf
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)
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)
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)
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)
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)
Behera, L., Kar, I.: Intelligent Systems and control principles and applications. Oxford University Press (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Yadav, A.P., Kumar, A., Behera, L. (2013). RNN Based Solar Radiation Forecasting Using Adaptive Learning Rate. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2013. Lecture Notes in Computer Science, vol 8298. Springer, Cham. https://doi.org/10.1007/978-3-319-03756-1_40
Download citation
DOI: https://doi.org/10.1007/978-3-319-03756-1_40
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03755-4
Online ISBN: 978-3-319-03756-1
eBook Packages: Computer ScienceComputer Science (R0)