文献类型： 会议论文

第一作者： Ali Lahouar

作者： Ali Lahouar ¹ ; Jaleleddine Ben Hadj Slama ¹ ;

作者机构： 1.Research Unit Advanced Systems in Electrical Engineering (SAGE) National Engineering School of Sousse (Eniso), University of Sousse Technopole de Sousse, Sousse, Tunisia

关键词： component;wind speed and direction forecasting;wind farm;support vector machines;regression

会议名称： International Renewable Energy Congress

主办单位：

页码： 725-730

摘要： Predicting wind speed and direction is one of the most important and critic tasks in a wind farm, since wind turbine blades motion and thus energy production is closely related to wind behaviour. Machine learning techniques are often used to predict the non-linear wind evolution. In this context, this paper proposes a short term wind data prediction model based on support vector machines in their regression mode, which have the advantage of being simple, fast and well adapted for the short term. This research tries also to prove how wind direction may influence power generation, and why it is important to predict it. A real data set of wind speed and direction historical values is used, from Sidi Daoud wind farm, north-eastern Tunisia, in order to evaluate the proposed model. This forecasting system predicts wind speed and direction for the short term, from one to 10 hours in advance, using a set of past samples.

分类号： X24-53