A novel short-term multi-input–multi-output prediction model of wind speed and wind power with LSSVM based on improved ant colony algorithm optimization

2018 ◽  
Vol 22 (S2) ◽  
pp. 3293-3300 ◽  
Author(s):  
Jingxian Yang
Keyword(s):  
Wind Speed ◽  
Prediction Model ◽  
Wind Power ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Algorithm Optimization ◽  
Short Term ◽  
Improved Ant Colony Algorithm

Applied-Information Technology in Short-Term Wind Speed Forecast Model for Wind Farms Based on Ant Colony Optimization and BP Neural Network

2014 ◽  
Vol 662 ◽  
pp. 259-262 ◽  
Author(s):  
Qi Di Zhao ◽  
Yang Yu ◽  
Meng Meng Jia
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Ant Colony Optimization ◽  
Bp Neural Network ◽  
Ant Colony Algorithm ◽  
Back Propagation ◽  
Wind Farms ◽  
Forecast Model ◽  
Ant Colony ◽  
Short Term

To improve the short-term wind speed forecasting accuracy of wind farms, a prediction model based on back propagation (BP) neural network combining ant colony algorithm is built to predict short-term wind speed. The input variables of BP neural network predictive model are historical wind speeds, temperature, and air pressure. Ant colony algorithm is used to optimize the weights and bias of BP neural networks. Using the ant colony optimization BP neural network model to predict the future 1h wind speed, the simulation results show that the proposed method offers the advantages of high precision and fast convergence in contrast with BP neural network.

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