基于最小二乘支持向量机的光伏出力超短期预测  

Ultra-Short-term Forecasting for Photovoltaic Power Output Based on Least Square Support Vector Machine

作　　者：张华彬[1] 杨明玉[1] 

机构地区：[1]华北电力大学电气与电子工程学院,河北保定071003

出　　处：《现代电力》2015年第1期70-75,共6页Modern Electric Power

摘　　要：随着大规模光伏电站接入配网,为了减轻光伏出力的随机性对电网安全稳定运行的影响,有必要加强光伏出力预测研究。提出了一种基于最小二乘支持向量机(LSSVM)的光伏出力超短期预测模型,模型的输入考虑了待预测时段的最新气象信息,提前1h对每刻钟的光伏出力进行预测。为了能更精确地反映待预测日的天气情况,对影响光伏出力的每一气象因素,分别赋予一适当权值,通过计算加权欧氏距离确定各时段的训练样本。最后,利用含有突变情况的天气对训练好的模型进行了测试和评估。结果表明,所提模型预测精度较高,能够为电网调度部门制定合理调度计划提供一定的参考依据。With the connecting of large-scale photovoltaic （PV） power station to the distribution network,it is necessary to strengthen the study of photovoltaic power output prediction in order to mitigate the impacts of randomness on power system.An ultra-short-term forecasting model based on least square support vector machine （LS-SVM） is proposed.To predict the PV output for every quarter ahead of 1h,the inputs of the model are the latest meteorologic information.To accurately reflect the weather condition of predicted day,a proper weight value is set to each meteorological factor which affects the PV output,then the training samples are determined by calculating the weighted Euclid distance.In the end,the trained model is tested and evaluated by using weather data with sudden changes.The results show that the proposed model has high precision,and can provide reference for dispatching department to formulate reasonable schedule.

关 键 词：最小二乘支持向量机 光伏发电系统 超短期预测 加权欧氏距离 相似时段 

分 类 号：TM615[电气工程—电力系统及自动化]