基于决策树理论的风电功率实时预测方法

杨 茂; 翟冠强

杨茂,翟冠强.基于决策树理论的风电功率实时预测方法[J].电测与仪表,2018,55(11):120-124.
Yang Mao,Zhai Guanqiang.Real-time Wind Power Forecasting Method Based on Decision Tree Theory[J].Electrical Measurement & Instrumentation,2018,55(11):120-124.

基于决策树理论的风电功率实时预测方法

Real-time Wind Power Forecasting Method Based on Decision Tree Theory

DOI：

中文关键词: 风电功率实时预测组合模型序列特性决策树

英文关键词:wind power, real-time prediction, combinatorial model, sequence property, decision tree

基金项目:国家重点基础研究发展计划项目(973计划)(2013CB228201)；国家自然科学基金项目(51307017)；国家重点研发计划项目课题（2016YFB0900101）

作者中文名	作者英文名	单位
杨茂	Yang Mao	现代电力系统仿真控制与绿色电能新技术吉林省重点实验室东北电力大学
翟冠强	Zhai Guanqiang	现代电力系统仿真控制与绿色电能新技术吉林省重点实验室东北电力大学

摘要点击次数: 1731

中文摘要:

风电功率的不确定性会对电网造成冲击,风电功率实时预测是缓解电网调频压力,保证电网稳定运行的重要手段。通过分析两种单一预测模型的特点,提出一种基于历史数据和NWP数据相结合的组合预测模型。分析历史数据的序列特征和适用的预测方法,建立决策树分类模型,在预测时通过实时数据序列特征分析,选择最佳预测方法。针对两不同风电场的数据进行预测分析,结果表明,组合模型能预测精度高于单一模型,通过决策树模型进行实时序列特征分析并选配最佳预测模型能有效提高预测精度。

英文摘要:

Uncertainty of wind power will cause the impact to power grid. Wind power real-time forecasting can ease the power grid and ensure the stability of the grid. Based on the analysis of the characteristics of two kinds of single forecasting models, proposes a combined forecasting model based on historical data and NWP data. The sequence feature of the historical data and the applicable forecasting method are analyzed. The decision tree classification model is established. The best forecasting method is selected by real-time data sequence feature analysis. The results show that the combined model can forecast the accuracy higher than that of the single model. Real - time sequence feature analysis and matching the best forecasting model can improve the prediction accuracy by the decision tree model.

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