Name
Title | ||
|---|---|---|
A Data Mining-Based Wind Power Forecasting Method: Results for Wind Power Plants in Turkey |
Abstract | ||
|---|---|---|
With the huge technological and industrial developments in recent years, the electricity demand of all countries has been increasing day by day. In order to supply the electricity needs, countries have been looking for ways of benefitting from their renewable energy sources efficiently and wind energy is an important and ubiquitous renewable energy source. However, due to wind's discontinuity and unstable characteristics, a reliable wind forecasting system is crucial not only for transmission system operators but also wind power plant WPP owners. This paper presents a reliable forecasting method based on data mining approaches. The method uses numerical weather predictions and past power measurements of the WPPs as input and it produces hourly short-term wind power forecasts for the WPPs for a time period of 48 hours. The method has been tested in the Wind Power Monitoring and Forecast Center Ră﾿TM project of Turkey for a duration of six months for 14 WPPs. The proposed model achieves better accuracy performance rates than those of the other well-known forecasting models for seven of WPPs selected for the testing procedure by the General Directorate of Renewable Energy in Turkey. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-40131-2_23 | DaWaK |
Keywords | Field | DocType |
clustering,wind power | Electricity demand,Data mining,Renewable energy,Transmission system operator,Offshore wind power,Electricity,Computer science,Wind power forecasting,Cluster analysis,Wind power | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mehmet Kemal Özkan | 1 | 4 | 2.99 |
| Dilek Küçük | 2 | 109 | 10.24 |
| Erman Terciyanli | 3 | 0 | 0.34 |
| Serkan Buhan | 4 | 5 | 2.23 |
| Turan Demirci | 5 | 0 | 2.37 |
| Pinar Karagoz | 6 | 154 | 28.34 |