摘要：能源问题是人类可持续发展过程中急需解决的重大问题。随着常规能源濒临枯竭，可再生能源越来越受到世界各国的重视，风能在可再生能源中占据重要的位置。风力发电从技术的成熟性和经济可行性看，在可再生能源中具有良好的前景。
由于风力发电具有波动性、间隙性和随机性的特点，大容量的风力发电接入电网，对电力系统的安全、稳定运行带来严峻的挑战。对于风速功率进行预测，是解决这一问题的有效途径。
支持向量机（SVM）是一种新型机器学习方法，由于其出色的学习性能，在近几年来已经成为一个十分活跃的研究领域。因此，在研究支持向量机和最小二乘支持向量机相关理论的基础上，为了能够让LS-SVM能够获得更好的得到回归效果，用交叉验证法对模型的参数进行了优化选择，从而提高最小二乘支持向量机的回归精度和泛化能力.
关键词：支持向量机，风力发电，风速预测 ，LS-SVM，交叉验证
Abstract: Energy problem is major issues to be settled urgently in process of human sustainable development. With the verge of depletion of conventional energy, renewable energy is receiving increasing attention around the world. Wind energy occupies an important position in the development of renewable energy. Wind power generation has good prospects in renewable energy sources form ripe technology and feasible financial condition.
Due to the features of being fluctuant, intermittent, and stochastic of wind power, interconnection of large capacity wind farms wind with the power grid will bring about impact on the safety and stability of power systems. To predict the wind speed is an effective way to solve the problem.
Support vector machine(SVM), a new method developed in recent years, is an advanced research field in machine learning. Therefore, as some collected data is far cry from training data or can be classified incorrectly in feature space, weight method is recommended to the Least Square Support Vector Machine, and a method of setting weight is given. The sample data is optimized selected by set weight.
Key words: Support Vector Machine, Wind Power, Wind Speed Prediction, Least Square Support Vector Machine, Cross Validate
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