Name
Title | ||
|---|---|---|
Short-term wind power prediction based on wavelet decomposition and extreme learning machine |
Abstract | ||
|---|---|---|
Wind energy has been widely used as a renewable green energy all over the world. Due to the stochastic character in wind, the uncertainty in wind generation is so large that power grid with safe operation is challenge. So it is very significant to design an algorithm to forecast wind power for grid operator to rapidly adjust management planning. In this paper, based on the strong randomness of wind and the short precision of BP network forecasting, Short-Term Power Prediction of a Wind Farm Based on Wavelet Decomposition and Extreme Learning Machine (WD-ELM) is proposed. Signal was decomposed into several sequences in different band by wavelet decomposition. Decomposed time series were analyzed separately, then building the model for decomposed time series with ELM to predict. Then the predicted results were added. Through a wind-power simulation analysis of a wind farm in Inner Mongolia, the result shows that the method in this paper has higher power prediction precision compared with other methods. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-31362-2_71 | ISNN (2) |
Keywords | Field | DocType |
wind energy,wind generation,power grid,short-term wind power prediction,higher power prediction precision,renewable green energy,grid operator,wind farm,wavelet decomposition,extreme learning machine,decomposed time series,wind power | Wavelet decomposition,Renewable energy,Extreme learning machine,Computer science,Power grid,Artificial intelligence,Operator (computer programming),Wind power,Grid,Machine learning,Randomness | Conference |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xin Wang | 1 | 5 | 3.76 |
| Yihui Zheng | 2 | 1 | 1.79 |
| Lixue Li | 3 | 1 | 0.77 |
| Lidan Zhou | 4 | 31 | 5.78 |
| Gang Yao | 5 | 31 | 5.78 |
| Ting Huang | 6 | 0 | 0.34 |