Hybrid PSO-BP Neural Network Approach for Wind Power Forecasting

Due to the intermittence and fluctuation of wind power generation, accurate forecasting of wind power is of great significance for the safe and stable operation of wind power integrated system. In the paper, a new wind power forecasting method combining particle swarm optimization (PSO) and back-propagation (BP) neural network is proposed. BP neural network structure was constructed according the number of input data, and PSO algorithm is used to get the optimal initial weights and biases of BP neural network, which can effectively overcome the shortcoming of BP neural network that it is easy to fall into local optimal solution, and increase the convergence speed of BP neural network. Considering the integrity of training data, the PSO-BP neural network was trained to use full year data in 2011. The original BP, PSO-BP, GA-BP and wavelet-BP neural network is applied for 6-h, 1-day, 3-day wind power forecasting in May and December of 2012, respectively. The compared results show that the mean absolute error(MAE) and the root mean square error(RMSE) of wind power forecasting based on PSO-BP neural network are clearly less than that based on original BP, GA-BP and wavelet-BP neural network.

[1]  Paras Mandal,et al.  A review of wind power and wind speed forecasting methods with different time horizons , 2010, North American Power Symposium 2010.

[2]  Xiaolan Wang,et al.  Multiscale prediction of wind speed and output power for the wind farm , 2012 .

[3]  Yi Zeng,et al.  Multifactor-influenced energy consumption forecasting using enhanced back-propagation neural network , 2017 .

[4]  M. Genton,et al.  Powering Up With Space-Time Wind Forecasting , 2010 .

[5]  Meenu Gupta.  Wind Power Forecasting: A Review of Statistical Models , 2013 .

[6]  N. Strachan,et al.  The critical role of the industrial sector in reaching long-term emission reduction, energy efficiency and renewable targets , 2016 .

[7]  N. Scarlat,et al.  Renewable energy policy framework and bioenergy contribution in the European Union – An overview from National Renewable Energy Action Plans and Progress Reports , 2015 .

[8]  Zhile Yang,et al.  Time series wind power forecasting based on variant Gaussian Process and TLBO , 2016, Neurocomputing.

[9]  Ming Diao,et al.  Performance enhancement of INS/CNS integration navigation system based on particle swarm optimization back propagation neural network , 2015 .

[10]  Abbas Khosravi,et al.  A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings , 2015 .

[11]  Carlos Gershenson,et al.  Wind speed forecasting for wind farms: A method based on support vector regression , 2016 .

[12]  Zhang Hong,et al.  The Application of the Pso Based BP Network in Short-Term Load Forecasting , 2012 .

[13]  K. Satheesh Kumar,et al.  Improved week-ahead predictions of wind speed using simple linear models with wavelet decomposition , 2016 .

[14]  J. Contreras,et al.  Renewable energy policy performance in reducing CO2 emissions , 2016 .

[15]  Rui Castro,et al.  Wind Speed and Wind Power Forecasting using Statistical Models: AutoRegressive Moving Average (ARMA) and Artificial Neural Networks (ANN) , 2012 .

[16]  Chao Ren,et al.  Optimal parameters selection for BP neural network based on particle swarm optimization: A case study of wind speed forecasting , 2014, Knowl. Based Syst..

[17]  A. P. Mathews,et al.  Renewable Energy Technologies: Panacea for World Energy Security and Climate Change? , 2014, ANT/SEIT.

[18]  Hui Liu,et al.  Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks , 2015 .