基于数据驱动的超短期风电功率预测综述

Review on ultra-short term wind power forecasting based on data-driven approach

以数据驱动为主要特征的超短期风功率预测是大规模风电并网运行的关键基础之一。按照预测流程,从数据挖掘、机器学习算法及风速-功率曲线等角度分析现有数据驱动方法的思想及局限性。总结离线数据驱动/深度学习算法和在线应用的预测思路,给出风电场数据筛选的评价手段,归纳深度学习算法的最新研究进展。最后分析超短期风功率预测的当前定位:“由模型驱动向数据驱动过渡,由机器学习算法向深度学习算法转移”,并指出合理的算法更迭和深层次的数据融合将是未来的研究趋势。

Ultra-short-term wind power forecasting based on data-driven approach is one of the key foundations when large scale wind power integrated into the power grid.According to the forecasting process,the basic thoughts and limitations of existing methods are analyzed from the point of view of data mining approach,machine learning algorithms and wind power curve.Furthermore,the new prediction idea of offline data-driven/deep learning algorithms and online application is concluded,the evaluation methods of information screening are given,the latest research progress of deep learning algorithms in data-driven forecasting is summarized.Finally,the current position of ultra-short term wind power forecasting is summarized,that is transition from model driven to data-driven and transfer from machine learning to deep learning,and it is pointed out that the alternation of algorithms and data fusion will be the research trends in the future.