Development of GRBFN with global structure for PV generation output forecasting

This paper presents a new method for forecasting of PV generation output. The output of PV systems is significantly affected by the weather conditions. As a result, forecasting of PV systems generation output is one of the most difficult time series forecasting. However, power system operators require more accurate prediction model to deal with power system operation such as economic load dispatching, unit commitment, etc. The proposed method makes use of a hybrid intelligent system that consists of Generalized Radial Basis Function Network (GRBFN), Deterministic Annealing (DA), and Evolutionary Particle Swarm Optimization (EPSO). GRBFN is one of artificial neural networks (ANNs) that provide good performance with complicated nonlinear time series. DA is used for determining the center and width of radial basis functions in GRBFN. EPSO is useful for optimizing weights between neurons in GRBFN to improve the performance from a standpoint of global optimization. Also, this paper applies the weight decay method to the cost function to avoid overfitting for learning data of nonlinear complicated data. The proposed method is successfully applied to real data of the PV system in Japan.

[1]  Thong Ngee Goh,et al.  Stochastic modeling and forecasting of solar radiation data , 1977 .

[2]  Hiroyuki Mori,et al.  A fault detection technique with preconditioned ANN in power systems , 2002, IEEE/PES Transmission and Distribution Conference and Exhibition.

[3]  Tomonobu Senjyu,et al.  Application of neural network to 24-hour-ahead generating power forecasting for PV system , 2008, PES 2008.

[4]  Vladimiro Miranda,et al.  EPSO - best-of-two-worlds meta-heuristic applied to power system problems , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[5]  M. Etezadi-Amoli,et al.  Practical approach for sub-hourly and hourly prediction of PV power output , 2010, North American Power Symposium 2010.

[6]  Vladimiro Miranda,et al.  EPSO-evolutionary particle swarm optimization, a new algorithm with applications in power systems , 2002, IEEE/PES Transmission and Distribution Conference and Exhibition.

[7]  A. Moreno-Munoz,et al.  Short term forecasting of solar radiation , 2008, 2008 IEEE International Symposium on Industrial Electronics.

[8]  Geoffrey C. Fox,et al.  A deterministic annealing approach to clustering , 1990, Pattern Recognit. Lett..

[9]  H. Mori,et al.  Deterministic Annealing Clustering for ANN-Based Short-Term Load Forecasting , 2001, IEEE Power Engineering Review.

[10]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[11]  M. J. D. Powell,et al.  Radial basis functions for multivariable interpolation: a review , 1987 .

[12]  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.

[13]  A. Mellit,et al.  Artificial neural network model for prediction solar radiation data: application for sizing stand-alone photovoltaic power system , 2005, IEEE Power Engineering Society General Meeting, 2005.

[14]  Ting-Chung Yu,et al.  The forecast of the electrical energy generated by photovoltaic systems using neural network method , 2011, 2011 International Conference on Electric Information and Control Engineering.

[15]  Chul-Hwan Kim,et al.  Application of neural network to 24-hour-ahead generating power forecasting for PV system , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[16]  Gordon Reikard.  Predicting solar radiation at high resolutions: A comparison of time series forecasts , 2009 .

[17]  H. Mekki,et al.  Modeling and simulation of photovoltaic panel based on artificial neural networks and VHDL-language , 2007, 2007 14th IEEE International Conference on Electronics, Circuits and Systems.

[18]  E. Assareh,et al.  Solar radiation forecasting based on meteorological data using artificial neural networks , 2009, 2009 7th IEEE International Conference on Industrial Informatics.

[19]  Geoffrey E. Hinton.  Learning Translation Invariant Recognition in Massively Parallel Networks , 1987, PARLE.