电力系统自动化2017，Vol.41Issue(21)：33-39,7.DOI:10.7500/AEPS20170217006
微电网光伏发电的Adaboost天气聚类超短期预测方法
Ultra-short-term Photovoltaic Power Forecasting in Microgrid Based on Adaboost Clustering
摘要
Abstract
The accuracy of photovoltaic (PV) power generation prediction in the microgrid has high relativity with the weather condition.Under cloudy and rainy conditions,random fluctuations of meteorological factors result in low precision of the ultra-short-term power prediction.For this reason,a modified model based on combination of Adaboost clustering and Markov chain is proposed.First,an improved K-nearest neighbor(KNN)classifier is trained with the characteristic variables extracted from solar radiation using the moving average method.To improve the prediction accuracy of cloudy and rainy days,the attenuation coefficient of solar radiation is introduced to modify the Hottel model.A weighted Markov chain model is developed to predict the microgrid PV generation subsequently.The simulation results indicate that the proposed model can appreciably improve the precision of power prediction under different weather conditions and is of great significance to real-time economical dispatch.关键词
光伏发电/微电网/超短期预测/衰减系数/AdaboostKey words
photovoltaic power generation/microgrid/ultra-short-term power output forecasting/attenuation coefficient/Adaboost引用本文复制引用
谭津,邓长虹,杨威,梁宁,李丰君..微电网光伏发电的Adaboost天气聚类超短期预测方法[J].电力系统自动化,2017,41(21):33-39,7.基金项目
国家重点研发计划资助项目(2017YFB0903700, 2017YFB0903705) （2017YFB0903700, 2017YFB0903705）
武汉市科技创新计划资助项目(2013072304020824).This work is supported by National Key Research and Development Program of China (No. 2017YFB0903700, No.2017YFB0903705) and Science and Technology Project of Wuhan City(No.2013072304020824). （2013072304020824）
