Solar and wind forecasting by NARX neural networks

The nonlinear autoregressive network with exogenous input (NARX) is used to perform hourly solar irradiation and wind speed forecasting, according to a multi-step ahead approach. Temperature has been considered as the exogenous variable. The NARX topology selection is supported by a combined use of two techniques: (1) a genetic algorithm (GA)-based optimization technique and (2) a method that determines the optimal network architecture by pruning (optimal brain surgeon (OBS) strategy). The considered variables are observed at hourly scale in a seven year dataset and the forecasting is done for several time horizons in the range from 8 to 24 h ahead.

[1]  Soteris A. Kalogirou,et al.  Artificial intelligence techniques for sizing photovoltaic systems: A review , 2009 .

[2]  Goran Andersson,et al.  Impacts of forecast accuracy on grid integration of renewable energy sources , 2013, 2013 IEEE Grenoble Conference.

[3]  Abbas Khosravi,et al.  Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[4]  Gianpaolo Vitale,et al.  Solar Radiation Estimate and Forecasting by Neural Networks for Smart Grid Energy Management , 2013 .

[5]  D. Janaki Ram,et al.  Constraint meta-object: a new object model for distributed collaborative designing , 1997, IEEE Trans. Syst. Man Cybern. Part A.

[6]  Lars Kai Hansen,et al.  Linear unlearning for cross-validation , 1996, Adv. Comput. Math..

[7]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[8]  Babak Hassibi,et al.  Second Order Derivatives for Network Pruning: Optimal Brain Surgeon , 1992, NIPS.

[9]  Gianpaolo Vitale,et al.  Estimation and Forecast of Wind Power Generation by FTDNN and NARX-net based models for Energy Management Purpose in Smart Grids , 2014 .

[10]  Lawrence Davis,et al.  Training Feedforward Neural Networks Using Genetic Algorithms , 1989, IJCAI.

[11]  Lennart Ljung,et al.  System identification toolbox for use with MATLAB , 1988 .

[12]  Hava T. Siegelmann,et al.  Computational capabilities of recurrent NARX neural networks , 1997, IEEE Trans. Syst. Man Cybern. Part B.

[13]  R. Inman,et al.  Solar forecasting methods for renewable energy integration , 2013 .

[14]  Dipti Srinivasan,et al.  Forecasting Solar and Wind data using Dynamic Neural Network Architectures for a Micro-Grid ensemble , 2013, 2013 IEEE Computational Intelligence Applications in Smart Grid (CIASG).