基于模式识别的风电场风速和发电功率预测

风电场风速预测对电力系统的交易计划和可靠运行起着非常重要的作用。根据风的形成机理、影响因素及变化规律，提出了一种基于模式识别技术选取风速样本，利用自适应模糊神经网络法（ANFIS）进行风速预测的方法，ANFIS利用混合学习算法训练网络的前件参数和结论参数，然后输入选取的风速样本于训练好的自适应模糊神经网络中进行风速预测。以美国夏威夷Maui岛1994年的风速数据为例，对上述方法进行验证，结果表明该方法具有一定的实用性。

Wind speed and generated power forecasting based on pattern recognition in wind farm

Wind speed forecasting is very important to the transaction planning and the operation reliability of power system in wind farm.According to the mechanism that the wind is formed, the influencing factor and its variation rule, a method of pattern recognition and adaptive neuron-fuzzy inference system for wind speed forecasting is presented in this paper. The hybrid algorithm is used to train the parameter of the fuzzy inference system. Inputted the related data to the trained model and anticipated wind speed is gotten. The Maui island of Hawaii is used as our case study, the predicted result shows applying this approach into practice would be valid.