论文标题
使用自动回归模型的时间序列分区的日前预测
Day-ahead prediction using time series partitioning with Auto-Regressive model
论文作者
论文摘要
在最近的研究人员中，风速预测在最近的研究人员中受到了很多关注，因为它在风能和分布的产生中受到了巨大好处。最大的挑战仍然是对风电场有效运行的风速准确预测。通过了解其潜在动态，可以大大改善风速预测。在本文中，我们提出了一种时间序列分区的方法，其中原始的10分钟风速数据被转换为二维秩序阵列（n x 144），其中n表示每日10分钟观察的天数。成功的时间序列分区后，对从10分钟的风速观察中提取的144个数据集计算了一个预测，然后使用自动回火（AR）过程将其合并在一起，以给出（n+1）ST天的预测。当AR模型与时间序列分区相结合时，计算结果显示出预测准确性的显着提高。
Wind speed forecasting has received a lot of attention in the recent past from researchers due to its enormous benefits in the generation of wind power and distribution. The biggest challenge still remains to be accurate prediction of wind speeds for efficient operation of a wind farm. Wind speed forecasts can be greatly improved by understanding its underlying dynamics. In this paper, we propose a method of time series partitioning where the original 10 minutes wind speed data is converted into a two-dimensional array of order (N x 144) where N denotes the number of days with 144 the daily 10-min observations. Upon successful time series partitioning, a point forecast is computed for each of the 144 datasets extracted from the 10 minutes wind speed observations using an Auto-Regressive (AR) process which is then combined together to give the (N+1) st day forecast. The results of the computations show significant improvement in the prediction accuracy when AR model is coupled with time series partitioning.
