Short-Term Wind Power Forecasting Using the Enhanced Particle Swarm Optimization Based Hybrid Method
AbstractHigh penetration of wind power in the electricity system provides many challenges to power system operators, mainly due to the unpredictability and variability of wind power generation. Although wind energy may not be dispatched, an accurate forecasting method of wind speed and power generation can help power system operators reduce the risk of an unreliable electricity supply. This paper proposes an enhanced particle swarm optimization (EPSO) based hybrid forecasting method for short-term wind power forecasting. The hybrid forecasting method combines the persistence method, the back propagation neural network, and the radial basis function (RBF) neural network. The EPSO algorithm is employed to optimize the weight coefficients in the hybrid forecasting method. To demonstrate the effectiveness of the proposed method, the method is tested on the practical information of wind power generation of a wind energy conversion system (WECS) installed on the Taichung coast of Taiwan. Comparisons of forecasting performance are made with the individual forecasting methods. Good agreements between the realistic values and forecasting values are obtained; the test results show the proposed forecasting method is accurate and reliable.
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Chang, W.-Y. Short-Term Wind Power Forecasting Using the Enhanced Particle Swarm Optimization Based Hybrid Method. Energies 2013, 6, 4879-4896.
Chang W-Y. Short-Term Wind Power Forecasting Using the Enhanced Particle Swarm Optimization Based Hybrid Method. Energies. 2013; 6(9):4879-4896.Chicago/Turabian Style
Chang, Wen-Yeau. 2013. "Short-Term Wind Power Forecasting Using the Enhanced Particle Swarm Optimization Based Hybrid Method." Energies 6, no. 9: 4879-4896.