Abstract
An on-line prediction algorithm able to estimate, over a determined time horizon, the solar irradiation of a specific site is considered. The learning algorithm is based on Radial Basis Function (RBF) networks and combines the growing criterion and the pruning strategy of the minimal resource allocating network technique. An adaptive extended Kalman filter is used to update all the parameters of the Neural Network (NN). The on-line learning mechanism avoids the initial training of the NN with a large data set. The proposed solution has been experimentally tested on a 14 kWp PhotoVoltaic (PV) plant and results are compared to a classical RBF neural network.
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References
http://www.3bmeteo.com (2012)
Antonini, P., Ippoliti, G., Longhi, S.: Learning control of mobile robots using a multiprocessor system. Control Engineering Practice 14(11), 1279–1295 (2006)
Ben Salah, C., Ben Mabrouk, A., Ouali, M.: Wavelet autoregressive forecasting of climatic parameters for photovoltaic systems. In: 2011 8th International Multi-Conference on Systems, Signals and Devices (SSD), pp. 1–6 (March 2011)
Cavalletti, M., Ippoliti, G., Longhi, S.: Lyapunov-based switching control using neural networks for a remotely operated vehicle. International Journal of Control 80(7), 1077–1091 (2007)
Cavalletti, M., Ippoliti, G., Longhi, S.: Intelligent control for a remotely operated vehicle. International Journal of Systems Science 40(11), 1099–1114 (2009)
Chen, S., Cowan, C., Grant, P.: Orthogonal least squares learning algorithm for radial basis function networks. IEEE Transactions on Neural Networks 2(2), 302–309 (1991)
Ciabattoni, L., Ippoliti, G., Longhi, S., Cavalletti, M., Rocchetti, M.: Solar irradiation forecasting using RBF networks for PV systems with storage. In: 2012 IEEE International Conference on Industrial Technology (ICIT), pp. 699–704 (March 2012)
Corradini, M., Ippoliti, G., Longhi, S., Marchei, D., Orlando, G.: A quasi-sliding mode observer-based controller for PMSM drives. Asian Journal of Control, 1–8 (2012), http://dx.doi.org/10.1002/asjc.555
Dorvlo, A.S., Jervase, J.A., Al-Lawati, A.: Solar radiation estimation using artificial neural networks. Applied Energy 71(4), 307–319 (2002)
Giantomassi, A., Ippoliti, G., Longhi, S., Bertini, I., Pizzuti, S.: On-line steam production prediction for a municipal solid waste incinerator by fully tuned minimal RBF neural networks. Journal of Process Control 21(1), 164–172 (2011)
Hunt, K., Sbarbaro, D., Zbikowski, R., Gawthrop, P.: Neural networks for control systems – A survey. Automatica 28(6), 1083–1112 (1992)
http://www.ilmeteo.it (2012)
Jetto, L., Longhi, S., Venturini, G.: Development and experimental validation of an adaptive extended Kalman filter for the localization of mobile robots. IEEE Transactions on Robotics and Automation 15(2), 219–229 (1999)
Karner, O.: ARIMA representation for daily solar irradiance and surface air temperature time series. Journal of Atmospheric and Solar-Terrestrial Physics 71(8-9), 841–847 (2009)
Lange, M., Focken, U.: Physical approach to short term wind power prediction. Springer, New-York (2006)
Lorenz, E., Hurka, J., Heinemann, D., Beyer, H.: Irradiance forecasting for the power prediction of grid-connected photovoltaic systems. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2(1), 2–10 (2009)
Palensky, P., Dietrich, D.: Demand side management: Demand response, intelligent energy systems, and smart loads. IEEE Transactions on Industrial Informatics 7(3), 381–388 (2011)
http://www.sielups.com (2012)
http://www.sma-italia.com (2012)
Sundararajan, N., Saratchandran, P., Li, Y.: Fully tuned radial basis function neural networks for flight control. Kluver Academic, London (2002)
Szabat, K., Orlowska-Kowalska, T.: Performance improvement of industrial drives with mechanical elasticity using nonlinear adaptive Kalman filter. IEEE Transactions on Industrial Electronics 55(3), 1075–1084 (2008)
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Ciabattoni, L., Ippoliti, G., Longhi, S., Pirro, M., Cavalletti, M. (2013). Solar Irradiation Forecasting for PV Systems by Fully Tuned Minimal RBF Neural Networks. In: Apolloni, B., Bassis, S., Esposito, A., Morabito, F. (eds) Neural Nets and Surroundings. Smart Innovation, Systems and Technologies, vol 19. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35467-0_29
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DOI: https://doi.org/10.1007/978-3-642-35467-0_29
Publisher Name: Springer, Berlin, Heidelberg
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