Abstract
The solar energy is a well alternative for covering the high electrical demand, and it starts to be integrated into the energetic grid infrastructure. High forecast accuracy can help in the management of industrial strategies. We present an approach that combines the potential of a Neural Network named Echo State Networks (ESN) and a well-known optimisation technique named Simulating Annealing (SA). We use the SA technique for selecting the meteorological variables relevant in the forecasting task and the ESN as forecasting model. We present the results evaluating our approach on a public dataset.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Pedro, H.T.C., Coimbra, C.F.M.: Assessment of forecasting techniques for solar power production with no exogenous inputs. Solar Energy 86(7), 2017–2028 (2012)
Basterrech, S., Prokop, L., Burianek, T., Misak, S.: Optimal design of neural tree for solar power prediction. In: 15th International Scientific Conference on Electric Power Engineering (EPE), Proccedings of the 2014, pp. 273–278, May 2014
Basterrech, S., Zjavka, L., Prokop, L., Misak, S.: Irradiance prediction using echo state queueing networks and differential polynomial neural networks. In: 13th International Conference on Intelligent Systems Design and Applications (ISDA), 2013, pp. 271–276, December 2013
Diagne, M., David, M., Lauret, P., Boland, J., Schmutz, N.: Review of solar irradiance forecasting methods and a proposition for small-scale insular grids. Renew. Sustain. Energy Rev. 27, 65–76 (2013)
Letendre, S., Makhyoun, M., Taylor, M.: Predicting solar power production: irradiance forecasting models, applications and future prospects. Technical report, Solar Electric Power Association, Washington, DC, USA, March 2014. www.solarelectricpower.org
Pelland, S., Remund, J., Kleissl, J., Oozeki, T., De Brabandere, K.: Photovoltaic, solar forecasting: state of art. Technical Report IEA-PVPS T14-01: 2013, International Energy Agency Photovoltaic Power Systems Programme (2013). http://www.iea-pvps.org
Andreas, A., Wilcox, S.: Aurora, Colorado (data). Technical Report DA-5500-56491, Solar Technology Acceleration Center (SolarTAC), Colorado, USA (2011). doi:10.5439/1052224
Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C++: The Art of Scientific Computing. Cambridge University Press, Cambridge (2002)
Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Networks 5(2), 157–166 (1994)
Jaeger, H.: The “echo state” approach to analysing and training recurrent neural networks. Technical report 148, German National Research Center for Information Technology (2001)
Lukos̆evic̆ius, M., Jaeger, H.: Reservoir computing approaches to recurrent neural network training. Comput. Sci. Rev. 3, 127–149 (2009)
Butcher, J.B., Verstraeten, D., Schrauwen, B., Day, C.R., Haycock, P.W.: Reservoir computing and extreme learning machines for non-linear time-series data analysis. Neural Networks 38, 76–89 (2013)
Acknowledgement
This work was supported by Grant of SGS No. SP2016/97, VŠB-Technical University of Ostrava, Czech Republic.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Basterrech, S. (2016). Experimental Analysis of Forecasting Solar Irradiance with Echo State Networks and Simulating Annealing. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2016. Lecture Notes in Computer Science(), vol 9692. Springer, Cham. https://doi.org/10.1007/978-3-319-39378-0_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-39378-0_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-39377-3
Online ISBN: 978-3-319-39378-0
eBook Packages: Computer ScienceComputer Science (R0)