지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국정보처리학회 JIPS(Journal of Information Processing Systems) JIPS(Journal of Information Processing Systems) 제14권 제6호
- 발행연도
- 2018.1
- 수록면
- 1,385 - 1,397 (13page)
이용수
초록· 키워드
오류제보하기
There are many factors that affect the wind speed. In addition, the randomness of wind speed also leads to lowprediction accuracy for wind speed. According to this situation, this paper constructs the short-timeforecasting model based on the least squares support vector machines (LSSVM) to forecast the wind speed. The basis of the model used in this paper is support vector regression (SVR), which is used to calculate theregression relationships between the historical data and forecasting data of wind speed. In order to improvethe forecast precision, historical data is clustered by cluster analysis so that the historical data whose changingtrend is similar with the forecasting data can be filtered out. The filtered historical data is used as the trainingsamples for SVR and the parameters would be optimized by particle swarm optimization (PSO). Theforecasting model is tested by actual data and the forecast precision is more accurate than the industrystandards. The results prove the feasibility and reliability of the model.
목차
등록된 정보가 없습니다.
참고문헌 (19)
참고문헌 신청
Y. Q. Liu / 2016 / Numerical weather prediction wind correction methods and its impact on computational fluid dynamics based wind power forecasting / Journal of Renewable and Sustainable Energy 8(3):1~14
S. Tohidi / 2010 / Influence of model simplifications and parameters on dynamic performance of grid connected fixed speed wind turbines / Proceedings of the 19th International Conference on Electrical Machines :1~7
D. P. Bertsekas / 1996 / Incremental least squares methods and the extended Kalman filter / SIAM Journal on Optimization 6(3):807~822
R. J. Kuligowski / 1998 / Localized precipitation forecasts from a numerical weather prediction model using artificial neural networks / Weather & Forecasting 13(4):1194~1204
L. Landberg / 2001 / Short-term prediction of local wind conditions / Journal of Wind Engineering and Industrial Aerodynamics 89(3-4):235~245
S. Tohidi / 2010 / Influence of model simplifications and parameters on dynamic performance of grid connected fixed speed wind turbines / Proceedings of the 19th International Conference on Electrical Machines :1~7
D. P. Bertsekas / 1996 / Incremental least squares methods and the extended Kalman filter / SIAM Journal on Optimization 6(3):807~822
R. J. Kuligowski / 1998 / Localized precipitation forecasts from a numerical weather prediction model using artificial neural networks / Weather & Forecasting 13(4):1194~1204
L. Landberg / 2001 / Short-term prediction of local wind conditions / Journal of Wind Engineering and Industrial Aerodynamics 89(3-4):235~245
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