An ANN-based Approach for Forecasting the Power Output of Photovoltaic System

Abstract With the increasing use of large-scale grid-connected photovoltaic system, accurate forecast approach for the power output of photovoltaic system has become an important issue. In order to forecast the power output of a photovoltaic system at 24-hour-ahead without any complex modeling and complicated calculation, an artificial neural network based approach is proposed in this paper. The improved back-propagation learning algorithm is adopted to overcome shortcomings of the standard back-propagation learning algorithm. Similar day selection algorithm based on forecast day information is proposed to improve forecast accuracy in different weather types. Forecasting results of a photovoltaic system show that the proposed approach has a great accuracy and efficiency for forecasting the power output of photovoltaic system.

[1]  R. Belmans,et al.  Voltage fluctuations on distribution level introduced by photovoltaic systems , 2006, IEEE Transactions on Energy Conversion.

[2]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[3]  Adnan Sözen,et al.  Forecasting based on neural network approach of solar potential in Turkey , 2005 .

[4]  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.

[5]  E. Arcaklioğlu,et al.  Use of artificial neural networks for mapping of solar potential in Turkey , 2004 .

[6]  S. Yamashiro,et al.  Novel Distributed Power Generating System of PV-ECaSS Using Solar Energy Estimation , 2007, IEEE Transactions on Energy Conversion.

[7]  Zerouak Hamza.  Applications of artificial neural-networks for energy systems , 2000 .

[8]  T. Funabashi,et al.  Application of Recurrent Neural Network to Short-Term-Ahead Generating Power Forecasting for Photovoltaic System , 2007, 2007 IEEE Power Engineering Society General Meeting.

[9]  Chul-Hwan Kim,et al.  Application of Neural Network to One-Day-Ahead 24 hours Generating Power Forecasting for Photovoltaic System , 2007, 2007 International Conference on Intelligent Systems Applications to Power Systems.

[10]  James P. Dunlop,et al.  Photovoltaic Systems , 2007 .

[11]  Bahman Kermanshahi,et al.  Recurrent neural network for forecasting next 10 years loads of nine Japanese utilities , 1998, Neurocomputing.

[12]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[13]  Chen Changsong,et al.  Forecasting power output for grid-connected photovoltaic power system without using solar radiation measurement , 2010, The 2nd International Symposium on Power Electronics for Distributed Generation Systems.