Abstract
Solar irradiance is an alternative of renewable resource that can be used for covering a relevant part of the growing demand of electrical energy. To have accurate solar irradiance predictions can help to integrate the solar power resources into the grid. We analyse the performance of an automatic procedure for selecting the most significant input features that impacts on the solar irradiance. The approach is based on a generalisation of swarm optimisation named Geometrical Particle Swarm Optimization (GPSO). Once, a good combination of weather information is defined, we use a reservoir computing model as forecasting technique. In particular, we use the Echo State Networks (ESN) model that is a Recurrent Neural Network often used for solving temporal learning problems. We evaluate our approach on a well-known public meteorological dataset obtaining promising results.
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References
Moraglio, A., Di Chio, C., Togelius, J., Poli, R.: Geometric particle swarm optimization. J. Artif. Evol. Appl. 2008, 11:1–11:14 (2008). http://dx.doi.org/10.1155/2008/143624
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)
Friedrich, T., He, J., Jansen, T., Moraglio, A.: Genetic and evolutionary computation. Theory Comput. Sci. 561, 1–2 (2015). http://dx.doi.org/10.1016/j.tcs.2014.11.022
Jaeger, H.: The “echo state” approach to analysing and training recurrent neural networks. German National Research Center for Information Technology. Technical report 148 (2001)
Chen, C., Duan, S., Cai, T., Liu, B.: Online 24-h solar power forecasting based on weather type classification using artificial neural network. Sol. Energy 85, 2856–2870 (2011)
Letendre, S., Makhyoun, M., Taylor, M.: Predicting solar power production: irradiance forecasting models, applications and future prospects. Solar Electric Power Association, Washington, DC, USA, Technical report, March 2014. www.solarelectricpower.org
Pelland, S., Remund, J., Kleissl, J., Oozeki, T., Brabandere, K.D.: Photovoltaic and solar forecasting: state of art. International Energy Agency Photovoltaic Power Systems Programme, Technical report IEA-PVPS T14-01:2013 (2013). http://www.iea-pvps.org
Basterrech, S., Prokop, L., Burianek, T., Misak, S.: Optimal design of neural tree for solar power prediction. In: Proceedings of the 2014 15th International Scientific Conference on Electric Power Engineering (EPE), 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: 2013 13th International Conference on Intelligent Systems Design and Applications (ISDA), pp. 271–276, December 2013
Pedro, H.T., Coimbra, C.F.: Assessment of forecasting techniques for solar power production with no exogenous inputs. Sol. Energy 86(7), 2017–2028 (2012)
Bacher, P., Madsen, H., Nielsen, H.A.: Online short-term solar power forecasting. Sol. Energy 83, 1772–1783 (2009)
Clerc, M., Kennedy, J.: The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6(1), 58–73 (2002)
Lukos̆evic̆ius, M., Jaeger, H.: Reservoir computing approaches to recurrent neural network training. Comput. Sci. Rev. 3, 127–149 (2009)
Jaeger, H., Lukos̆evic̆ius, M., Popovici, D., Siewert, U.: Optimization and applications of Echo State Networks with leaky-integrator neurons. Neural Netw. 20(3), 335–352 (2007)
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 Netw. 38, 76–89 (2013)
Andreas, A., Wilcox, S.: Aurora, colorado (data). Solar Technology Acceleration Center (SolarTAC), Colorado, USA, Technical report DA-5500-56491 (2011). doi:10.5439/1052224
Acknowledgement
This work is partially supported by Grant of SGS No. SP2016/97, VŠB - Technical University of Ostrava, Czech Republic.
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Basterrech, S. (2017). Geometric Particle Swarm Optimization and Reservoir Computing for Solar Power Forecasting. In: Matoušek, R. (eds) Recent Advances in Soft Computing. ICSC-MENDEL 2016. Advances in Intelligent Systems and Computing, vol 576. Springer, Cham. https://doi.org/10.1007/978-3-319-58088-3_9
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DOI: https://doi.org/10.1007/978-3-319-58088-3_9
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