Short-Term Wind Power Forecasting: A New Hybrid Model Combined Extreme-Point Symmetric Mode Decomposition, Extreme Learning Machine and Particle Swarm Optimization

Other literature type, Article English OPEN
Jianguo Zhou; Xuechao Yu; Baoling Jin;
(2018)
  • Publisher: Multidisciplinary Digital Publishing Institute
  • Journal: Sustainability (issn: 2071-1050)
  • Publisher copyright policies & self-archiving
  • Related identifiers: doi: 10.3390/su10093202
  • Subject: hybrid model | TJ807-830 | wind power | extreme-point symmetric mode decomposition | TD194-195 | Renewable energy sources | extreme learning machine | GE1-350 | Environmental sciences | particle swarm optimization | Environmental effects of industries and plants

The nonlinear and non-stationary nature of wind power creates a difficult challenge for the stable operation of the power system when it accesses the grid. Improving the prediction accuracy of short-term wind power is beneficial to the power system dispatching departmen... View more
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