Artificial Neural Networks to Predict the Power Output of a PV Panel

The paper illustrates an adaptive approach based on different topologies of artificial neural networks (ANNs) for the power energy output forecasting of photovoltaic (PV) modules. The analysis of the PV module’s power output needed detailed local climate data, which was collected by a dedicated weather monitoring system. The Department of Energy, Information Engineering, and Mathematical Models of the University of Palermo (Italy) has built up a weather monitoring system that worked together with a data acquisition system. The power output forecast is obtained using three different types of ANNs: a one hidden layer Multilayer perceptron (MLP), a recursive neural network (RNN), and a gamma memory (GM) trained with the back propagation. In order to investigate the influence of climate variability on the electricity production, the ANNs were trained using weather data (air temperature, solar irradiance, and wind speed) along with historical power output data available for the two test modules. The model validation was performed by comparing model predictions with power output data that were not used for the network's training. The results obtained bear out the suitability of the adopted methodology for the short-term power output forecasting problem and identified the best topology.

[1]  Giuseppina Ciulla,et al.  THE REDESIGN OF AN ITALIAN BUILDING TO REACH NET ZERO ENERGY PERFORMANCES: A CASE STUDY OF THE SHC TASK 40 ECBCS Annex 52 , 2011 .

[2]  C. Rodriguez,et al.  Dynamic stability of grid-connected photovoltaic systems , 2004, IEEE Power Engineering Society General Meeting, 2004..

[3]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[4]  T. H. Ortmeyer,et al.  Evaluation of neural network based real time maximum power tracking controller for PV system , 1995 .

[5]  Chung-Yuen Won,et al.  A new maximum power point tracker of photovoltaic arrays using fuzzy controller , 1994, Proceedings of 1994 Power Electronics Specialist Conference - PESC'94.

[6]  Ch. Sai Babu,et al.  COMPARISON OF MAXIMUM POWER POINT TRACKING ALGORITHMS FOR PHOTOVOLTAIC SYSTEM , 2011 .

[7]  Suttichai Premrudeepreechacharn,et al.  Maximum power point tracking using adaptive fuzzy logic control for grid-connected photovoltaic system , 2005 .

[8]  Giuseppina Ciulla,et al.  Application of Adaptive Models for the Determination of the Thermal Behaviour of a Photovoltaic Panel , 2013, ICCSA.

[9]  Tomonobu Senjyu,et al.  Neural-network-based maximum-power-point tracking of coupled-inductor interleaved-boost-converter-supplied PV system using fuzzy controller , 2003, IEEE Trans. Ind. Electron..

[10]  Valerio Lo Brano,et al.  On the experimental validation of an improved five-parameter model for silicon photovoltaic modules , 2012 .

[11]  S. Danaher,et al.  Erosion modelling using Bayesian regulated artificial neural networks , 2004 .

[12]  Bogdan M. Wilamowski,et al.  Microprocessor implementation of fuzzy systems and neural networks , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[13]  Takashi Hiyama,et al.  Neural network based estimation of maximum power generation from PV module using environmental information , 1997 .

[14]  John Duffy,et al.  Energy performance of net-zero and near net-zero energy homes in New England , 2013 .

[15]  M. Masoum,et al.  Theoretical and Experimental Analyses of Photovoltaic Systems with Voltage and Current-Based Maximum Power Point Tracking , 2002, IEEE Power Engineering Review.

[16]  Giuseppina Ciulla,et al.  An efficient analytical approach for obtaining a five parameters model of photovoltaic modules using only reference data , 2013 .

[17]  E. Skoplaki,et al.  ON THE TEMPERATURE DEPENDENCE OF PHOTOVOLTAIC MODULE ELECTRICAL PERFORMANCE: A REVIEW OF EFFICIENCY/ POWER CORRELATIONS , 2009 .

[18]  Giuseppina Ciulla,et al.  Forecasting the Cell Temperature of PV Modules with an Adaptive System , 2013 .

[19]  Takashi Hiyama,et al.  Identification of optimal operating point of PV modules using neural network for real time maximum power tracking control , 1995 .

[20]  Giuseppina Ciulla,et al.  An improved five-parameter model for photovoltaic modules , 2010 .

[21]  Antonio Messineo,et al.  On the Evaluation of Solar Greenhouse Efficiency in Building Simulation during the Heating Period , 2012 .

[22]  Valerio Lo Brano,et al.  Quality of wind speed fitting distributions for the urban area of Palermo, Italy , 2011 .

[23]  P.L. Chapman,et al.  Comparison of Photovoltaic Array Maximum Power Point Tracking Techniques , 2007, IEEE Transactions on Energy Conversion.

[24]  Tomonobu Senjyu,et al.  Maximum power point tracking of coupled inductor interleaved boost converter supplied PV system , 2003 .

[25]  H. Metin Ertunc,et al.  Prediction of the air temperature and humidity at the outlet of a cooling coil using neural networks , 2006 .

[26]  D.S. Kirschen,et al.  Impact on the Power System of a Large Penetration of Photovoltaic Generation , 2007, 2007 IEEE Power Engineering Society General Meeting.

[27]  Andres Barrado,et al.  Review of the maximum power point tracking algorithms for stand-alone photovoltaic systems , 2006 .

[28]  Mohammad A. S. Masoum,et al.  Closure on "Theoretical and experimental analyses of photovoltaic systems with voltage and current-based maximum power point tracking" , 2002 .

[29]  Faten H. Fahmy,et al.  Evaluation of a proper controller performance for maximum-power point tracking of a stand-alone PV system , 2003 .

[30]  Vagelis Vossos,et al.  Energy savings from direct-DC in U.S. residential buildings , 2014 .

[31]  Vincenzo Pacelli,et al.  An Artificial Neural Network Approach for Credit Risk Management , 2011, J. Intell. Learn. Syst. Appl..

[32]  Hee-Jun Kim,et al.  A Voltage Based Maximum Power Point Tracker for Low Power and Low Cost Photovoltaic Applications , 2009 .

[33]  A. Maldonado,et al.  Physical properties of ZnO:F obtained from a fresh and aged solution of zinc acetate and zinc acetylacetonate , 2006 .

[34]  Andrea Roli,et al.  A neural network approach for credit risk evaluation , 2008 .

[35]  Li Wang,et al.  Random fluctuations on dynamic stability of a grid-connected photovoltaic array , 2001, 2001 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.01CH37194).

[36]  Jun-Tae Kim,et al.  Performance Evaluation of DSC Windows for Buildings , 2013 .