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基于可加性模糊系统的负荷时间序列预测 被引量:2
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作者 刘耀年 曾令全 +1 位作者 张文生 李玉玲 《电工电能新技术》 CSCD 2002年第4期23-25,73,共4页
本文依据可加性模糊系统理论 ,提出了一种新的预测方法 ,利用聚类方法与有监督学习相结合的训练方法 ,提高了系统的函数逼近能力。仿真结果表明 ,系统学习速度快、预测精度高 ,在电力负荷时间序列预测中获得相当满意的结果。
关键词 可加性模糊系统 负荷时间序列预测 电力系统 聚类学习算法 有监督学习
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Knowledge mining collaborative DESVM correction method in short-term load forecasting 被引量:3
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作者 牛东晓 王建军 刘金朋 《Journal of Central South University》 SCIE EI CAS 2011年第4期1211-1216,共6页
Short-term forecasting is a difficult problem because of the influence of non-linear factors and irregular events.A novel short-term forecasting method named TIK was proposed,in which ARMA forecasting model was used t... Short-term forecasting is a difficult problem because of the influence of non-linear factors and irregular events.A novel short-term forecasting method named TIK was proposed,in which ARMA forecasting model was used to consider the load time series trend forecasting,intelligence forecasting DESVR model was applied to estimate the non-linear influence,and knowledge mining methods were applied to correct the errors caused by irregular events.In order to prove the effectiveness of the proposed model,an application of the daily maximum load forecasting was evaluated.The experimental results show that the DESVR model improves the mean absolute percentage error(MAPE) from 2.82% to 2.55%,and the knowledge rules can improve the MAPE from 2.55% to 2.30%.Compared with the single ARMA forecasting method and ARMA combined SVR forecasting method,it can be proved that TIK method gains the best performance in short-term load forecasting. 展开更多
关键词 load forecasting support vector regression knowledge mining ARMA differential evolution
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