| contributor author | Haiyang Zheng | |
| contributor author | Andrew Kusiak | |
| date accessioned | 2017-05-09T00:35:19Z | |
| date available | 2017-05-09T00:35:19Z | |
| date copyright | August, 2009 | |
| date issued | 2009 | |
| identifier issn | 0199-6231 | |
| identifier other | JSEEDO-28421#031011_1.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl/handle/yetl/141915 | |
| description abstract | In this paper, multivariate time series models were built to predict the power ramp rates of a wind farm. The power changes were predicted at 10 min intervals. Multivariate time series models were built with data-mining algorithms. Five different data-mining algorithms were tested using data collected at a wind farm. The support vector machine regression algorithm performed best out of the five algorithms studied in this research. It provided predictions of the power ramp rate for a time horizon of 10–60 min. The boosting tree algorithm selects parameters for enhancement of the prediction accuracy of the power ramp rate. The data used in this research originated at a wind farm of 100 turbines. The test results of multivariate time series models were presented in this paper. Suggestions for future research were provided. | |
| publisher | The American Society of Mechanical Engineers (ASME) | |
| title | Prediction of Wind Farm Power Ramp Rates: A Data-Mining Approach | |
| type | Journal Paper | |
| journal volume | 131 | |
| journal issue | 3 | |
| journal title | Journal of Solar Energy Engineering | |
| identifier doi | 10.1115/1.3142727 | |
| journal fristpage | 31011 | |
| identifier eissn | 1528-8986 | |
| keywords | Algorithms | |
| keywords | Data mining | |
| keywords | Support vector machines | |
| keywords | Time series | |
| keywords | Wind farms AND Tree (Data structure) | |
| tree | Journal of Solar Energy Engineering:;2009:;volume( 131 ):;issue: 003 | |
| contenttype | Fulltext | |
