A Combination Prediction Model for Wind Farm Output Power

摘要：

Wind powers volatility and intermittence have a profound impact on power systems security and economic operation.However,high-precision power prediction is the important prerequisite to reduce the influence of wind power on the power system.This paper illustrates a wind power prediction model based on time-series and back propagation artificial neural network (BP-ANN),considering wind speed,temperature,humidity,geographical conditions and other factors.Taking account of approximate linear relationship between wind speeds,the prediction model of wind speed was built based on time-series,and the model of wind speed-to-power was set up in the way of the nonlinear mapping relationship based on the method of BPANN.The paper predicts wind power based on the measured data of 24h ahead.By analyzing predicted data,it shows that the combined prediction model based on time-series and BP-ANN is effective.

关键词： wind speed:wind power:prediction:time-series:BPANN

作者: Feng Jiangxia Liang Jun Wang Chengfu

作者单位: School of Electrical Engineering of Shandong University Jinan,China

会议类型: 国际会议

会议名称: 2011 4th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies(第四届电力设施管制、重建及能源技术国际会议 DRPT 2011)

会议地点: 威海

会议语种:英文

页码: 1290-1294

在线出版日期: 2011-07-06（万方平台首次上网日期，不代表论文的发表时间）