基于误差叠加修正的改进短期风电功率预测方法  

Improved Short-term Wind Power Forecasting Method Based on Accumulative Error Correction

作　　者：茆美琴[1] 曹雨[1] 周松林[1] 

机构地区：[1]教育部光伏系统工程研究中心,合肥工业大学,安徽省合肥市230009

出　　处：《电力系统自动化》2013年第23期34-38,共5页Automation of Electric Power Systems

基　　金：国家重点基础研究发展计划(973计划)资助项目(2009CB219708);国家自然科学基金资助项目(50837001;51077033)~~

摘　　要：对风电功率进行较为准确的预测是合理调整含有风电的电力系统或微电网系统的调度策略,提高其运行稳定性与经济性的有效手段。在分析传统风电功率预测过程的基础上,从基本预测方法在功率预测过程中的使用策略角度出发,提出了不依赖于基本预测方法的新的改进预测思路。在这种改进方法中,增加了误差预测模型,对传统方法的预测值所包含的误差值进行预测,并将通过误差预测模型得到的预测误差与传统方法的预测值叠加作为改进方法的最终预测结果,并以反向传播(BP)神经网络作为基本预测方法对实际风电场进行实例验证分析。计算结果表明:提出的改进风电功率预测方法能够较大幅度地提高预测精度;提出的改进思路和传统改进思路不同,并不涉及基本预测方法内部特性且无需引入其他辅助方法,因而具有良好的通用性。Accurate forecasting of wind power is an effective way to rationally adjust the scheduling strategies and to improve the operation stability and hence the economy of the power system with wind power or microgrids.A new improved prediction idea which does not rely on the basic methods is proposed based on analysis of a traditional wind power prediction procedure and of the strategy of how to use basic prediction methods in the wind power prediction.In the proposed method,an additional model is developed to calculate the prediction error,and the final result is acquired by adding the predicted error values to the traditional predicted values.Taking back propagation（BP）neural network as a basic prediction method,the proposed method is validated by predicting a real wind farm output.Simulation results show that the improved wind power prediction method can improve the prediction accuracy to a certain extent.The proposed method neither relates to the internal characteristics of the basic methods nor requires auxiliary method,which is different from traditional improved ideas and thus has a good versatility.

关 键 词：风电功率预测 反向传播神经网络 误差预测 支持向量机 

分 类 号：TM614[电气工程—电力系统及自动化]