论文标题
使用随机森林学习方法的风速预测
Wind speed forecast using random forest learning method
论文作者
论文摘要
风速预测模型及其在风电场运营中的应用在文献中引起了极大的关注，因为它作为清洁能源的好处。在本文中，我们提出了时间序列机器学习方法，称为随机森林回归，以预测风速变化。相互信息和自动相关的计算值表明，风速值取决于过去的数据长达12小时。从前12小时值的两个数据中，使用合奏作为每个值的输入对随机森林模型进行了训练。计算出的均方根错误表明，可以使用两周数据训练的模型来对可靠的短期预测提前三年。
Wind speed forecasting models and their application to wind farm operations are attaining remarkable attention in the literature because of its benefits as a clean energy source. In this paper, we suggested the time series machine learning approach called random forest regression for predicting wind speed variations. The computed values of mutual information and auto-correlation shows that wind speed values depend on the past data up to 12 hours. The random forest model was trained using ensemble from two weeks data with previous 12 hours values as input for every value. The computed root mean square error shows that model trained with two weeks data can be employed to make reliable short-term predictions up to three years ahead.
