NN based support system for renewable energy forecasting

This paper proposes to prosumers a NN based decision support application for selecting an optimal forecasting tool for energy produced from renewable sources. The exploration and the assessment of criteria used for choosing a forecasting tool are made in the neural network (NN) framework. Firstly, the criteria for selecting the best forecasting tool are addressed. Secondly, the identified criteria are integrated in an object oriented software application, built using Matlab-Guide User Interface. In order to underline the effects of the users' decision making, in the third part, the forecasting performances of feed forward neural networks (FF-NN) are tested and evaluated.

[1]  Magdi S. Mahmoud,et al.  Cascaded artificial neural networks for short-term load forecasting , 1997 .

[2]  Carlos E. Pedreira,et al.  Neural networks for short-term load forecasting: a review and evaluation , 2001 .

[3]  G. Karady,et al.  Economic impact analysis of load forecasting , 1997 .

[4]  Rob J Hyndman,et al.  25 years of time series forecasting , 2006 .

[5]  Fred L. Collopy,et al.  Error Measures for Generalizing About Forecasting Methods: Empirical Comparisons , 1992 .

[6]  Robert Fildes,et al.  The evaluation of extrapolative forecasting methods , 1992 .

[7]  Osama A. Mohammed,et al.  Practical experiences with an adaptive neural network short-term load forecasting system , 1995 .

[8]  Ajith Abraham,et al.  Intelligent weather monitoring systems using connectionist models , 2002, Neural Parallel Sci. Comput..

[9]  Stephen L. Chiu,et al.  Selecting Input Variables for Fuzzy Models , 1996, J. Intell. Fuzzy Syst..

[10]  Tommy W. S. Chow,et al.  Neural network based short-term load forecasting using weather compensation , 1996 .

[11]  Heaton T. Jeff,et al.  Introduction to Neural Networks with Java , 2005 .

[12]  Saifur Rahman,et al.  Input variable selection for ANN-based short-term load forecasting , 1998 .

[13]  R. Jager,et al.  Interpolation issues in Sugeno-Takagi reasoning , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[14]  Eugenia Minca,et al.  Forcasting of Renewable Energy Load with Radial Basis Function (RBF) Neural Networks , 2011, ICINCO.

[15]  Otilia Elena Dragomir,et al.  An application oriented guideline for choosing a prognostic tool , 2009 .

[16]  Rafael Gouriveau,et al.  Medium term load forecasting using ANFIS predictor , 2010, 18th Mediterranean Conference on Control and Automation, MED'10.

[17]  Stéphane Ploix,et al.  A prediction system for home appliance usage , 2013 .

[18]  A. Titli,et al.  Fusion and hierarchy can help fuzzy logic controller designers , 1997, Proceedings of 6th International Fuzzy Systems Conference.

[19]  Slobodan P. Simonovic,et al.  Estimation of missing streamflow data using principles of chaos theory , 2002 .

[20]  J. Scott Armstrong,et al.  On the Selection of Error Measures for Comparisons Among Forecasting Methods , 2005 .

[21]  B. Hobbs,et al.  Analysis of the value for unit commitment of improved load forecasts , 1999 .