Solar irradiation forecasting using RBF networks for PV systems with storage

In this paper a Radial Basis Function (RBF) neural network is proposed to obtain the 24-hr forecast of the solar irradiation on the horizontal plane in the city of Ancona, Italy. This information is used to estimate the production of a PhotoVoltaic (PV) plant in order to provide the 'Gestore dei Servizi Energetici' (the main italian provider of energy services) with the power production profile of the next day. The company Energy Resources SPA has experimentally tested the proposed solution by a 14 KWp PV plant and a lithium battery pack. The battery pack is used to store the exceding power produced or to supply the lack of power compared with the reference.

[1]  R. G. Ross,et al.  Flat-plate photovoltaic array design optimization , 1980 .

[2]  Chokri Ben Salah,et al.  Wavelet autoregressive forecasting of climatic parameters for photovoltaic systems , 2011, Eighth International Multi-Conference on Systems, Signals & Devices.

[3]  Kevin Warwick.  Neural networks: an introduction , 1995 .

[4]  N. Sundararajan,et al.  Fully Tuned Radial Basis Function Neural Networks for Flight Control , 2001, The Springer International Series on Asian Studies in Computer and Information Science.

[5]  Paramasivan Saratchandran,et al.  Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm , 1998, IEEE Trans. Neural Networks.

[6]  Jonathan A. Wright,et al.  Adaptive Diurnal Prediction of Ambient Dry-Bulb Temperature and Solar Radiation , 2002 .

[7]  Gianluca Ippoliti,et al.  A Multiple Models Approach for Adaptation and Learning in Mobile Robots Control , 2006, J. Intell. Robotic Syst..

[8]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[9]  John C. Platt.  A Resource-Allocating Network for Function Interpolation , 1991, Neural Computation.

[10]  Visakan Kadirkamanathan,et al.  A Function Estimation Approach to Sequential Learning with Neural Networks , 1993, Neural Computation.

[11]  D.R. Hush,et al.  Progress in supervised neural networks , 1993, IEEE Signal Processing Magazine.

[12]  F. Gonzalez-Longatt,et al.  Model of Photovoltaic Module in Matlab TM , 2007 .

[13]  F. Girosi,et al.  Networks for approximation and learning , 1990, Proc. IEEE.

[14]  A. W. Jayawardena,et al.  Comparison of Multilayer Perceptron and radial Basis Function networks as tools for flood forecasting , 1996 .

[15]  Joseph A. Jervase,et al.  Solar radiation estimation using artificial neural networks , 2002 .

[16]  Peter J. Gawthrop,et al.  Neural networks for control systems - A survey , 1992, Autom..

[17]  Mukhtiar Ali Unar,et al.  Automatic steering of ships using neural networks , 1999 .

[18]  M. Lange,et al.  Physical Approach to Short-Term Wind Power Prediction , 2005 .

[19]  George W. Irwin,et al.  Neural network applications in control , 1995 .

[20]  O. Kärner,et al.  ARIMA representation for daily solar irradiance and surface air temperature time series , 2009 .

[21]  Sauro Longhi,et al.  Lyapunov-based switching control using neural networks for a remotely operated vehicle , 2007, Int. J. Control.

[22]  Shang-Liang Chen,et al.  Orthogonal least squares learning algorithm for radial basis function networks , 1991, IEEE Trans. Neural Networks.

[23]  Kevin Warwick,et al.  Dynamic Systems in Neural Networks , 1995 .

[24]  Sauro Longhi,et al.  Learning control of mobile robots using a multiprocessor system , 2006 .

[25]  Hans-Georg Beyer,et al.  Irradiance Forecasting for the Power Prediction of Grid-Connected Photovoltaic Systems , 2009, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[26]  Maria Letizia Corradini,et al.  Neural Networks Based Control of Mobile Robots: Development and Experimental Validation , 2003, J. Field Robotics.