Improving Short-Term Wind Power Prediction with Neural Network and ICA Algorithm and Input Feature Selection

According to this fact that wind is now a part of global energy portfolio and due to unreliable and discontinuous production of wind energy; prediction of wind power value is proposed as a main necessity. In recent years, various methods have been proposed for wind power prediction. In this paper the prediction structure involves feature selection and use of Artificial Neural Network (ANN). In this paper, feature selection tool is applied in filtering of inappropriate and irrelevant inputs of neural network and is performed on the biases of mutual information. After determining appropriate inputs, the wind power value for the next 24-hours is predicted using neural network in which BP algorithm and PSO and ICA evolutionary algorithms are used as training algorithm. With investigation and compare numerical results, better performance of PSO and ICA evolutionary algorithm is deduced with respect to BP algorithm. More accurate survey will result in more proper efficiency of imperialist competitive algorithm (ICA) in comparison to swarm particle algorithm. Thus, in this paper; accuracy of the wind power prediction for the next 24-hours is improved considerably using mutual information and providing an irrelevancy filter for reducing the input dimension by eliminating the irrelevant candidates and more effectively using Imperialist competitive evolutionary algorithm for training the neural network.

[1]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[2]  Mollaiy Berneti Shahram,et al.  An Imperialist Competitive Algorithm Artificial Neural Network Method to Predict Oil Flow Rate of the Wells , 2011 .

[3]  Wei-Jen Lee,et al.  Forecasting the Wind Generation Using a Two-Stage Network Based on Meteorological Information , 2009, IEEE Transactions on Energy Conversion.

[4]  Ruddy Blonbou,et al.  Very short-term wind power forecasting with neural networks and adaptive Bayesian learning , 2011 .

[5]  Farshid Keynia,et al.  A new hybrid iterative method for short‐term wind speed forecasting , 2011 .

[6]  Li Lingling,et al.  Wind Power Forecasting Based on Time Series and Neural Network , 2009 .

[7]  S.A.P. Kani,et al.  A new ANN-based methodology for very short-term wind speed prediction using Markov chain approach , 2008, 2008 IEEE Canada Electric Power Conference.

[8]  Patrick van der Smagt,et al.  Introduction to neural networks , 1995, The Lancet.

[9]  Ying-Pin Chang,et al.  A PSO Method With Nonlinear Time-Varying Evolution for Optimal Design of Harmonic Filters , 2009, IEEE Transactions on Power Systems.

[10]  A. Ouammi,et al.  Short term forecast of wind power by an artificial neural network approach , 2012, 2012 IEEE International Systems Conference SysCon 2012.

[11]  Chong-Ho Choi,et al.  Input feature selection for classification problems , 2002, IEEE Trans. Neural Networks.

[12]  G. Sideratos,et al.  Using Radial Basis Neural Networks to Estimate Wind Power Production , 2007, 2007 IEEE Power Engineering Society General Meeting.

[13]  H. M. I. Pousinho,et al.  An Artificial Neural Network Approach for Short-Term Wind Power Forecasting in Portugal , 2009, 2009 15th International Conference on Intelligent System Applications to Power Systems.

[14]  Caro Lucas,et al.  Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition , 2007, 2007 IEEE Congress on Evolutionary Computation.

[15]  Yuan-Kang Wu,et al.  A literature review of wind forecasting technology in the world , 2007, 2007 IEEE Lausanne Power Tech.

[16]  Wen-Yeau Chang,et al.  SHORT-TERM WIND POWER FORECASTING USING THE ENHANCED PARTICLE SWARM OPTIMIZATION BASED HYBRID METHOD , 2013 .

[17]  Ali Motie Nasrabadi,et al.  Artificial neural network weights optimization using ICA, GA, ICA-GA and R-ICA-GA: Comparing performances , 2011, 2011 IEEE Workshop On Hybrid Intelligent Models And Applications.

[18]  J. Mahmoudi,et al.  Short and mid-term wind power plants forecasting with ANN , 2012, 2012 Second Iranian Conference on Renewable Energy and Distributed Generation.

[19]  Maria Grazia De Giorgi,et al.  Error analysis of short term wind power prediction models , 2011 .

[20]  S. N. Singh,et al.  AWNN-Assisted Wind Power Forecasting Using Feed-Forward Neural Network , 2012, IEEE Transactions on Sustainable Energy.

[21]  N. Amjady,et al.  Day-Ahead Price Forecasting of Electricity Markets by Mutual Information Technique and Cascaded Neuro-Evolutionary Algorithm , 2009, IEEE Transactions on Power Systems.

[22]  Ioannis B. Theocharis,et al.  Locally recurrent neural networks for long-term wind speed and power prediction , 2006, Neurocomputing.

[23]  Xingpei Li,et al.  Short-term forecasting of wind turbine power generation based on Genetic Neural Network , 2010, 2010 8th World Congress on Intelligent Control and Automation.

[24]  E. Sreevalsan,et al.  WIND SPEED AND POWER PREDICTION USING ARTIFICIAL NEURAL NETWORKS , .

[25]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[26]  Wenzhong Gao,et al.  Wind power plant prediction by using neural networks , 2012, 2012 IEEE Energy Conversion Congress and Exposition (ECCE).

[27]  Pietro Vecchio,et al.  Wind energy prediction using a two-hidden layer neural network , 2010 .