基于FHNN相似日聚类自适应权重的短期电力负荷组合预测  

Short-term Power Load Combinatorial Forecast Adaptively Weighted by FHNN Similar-day Clustering

作　　者：牛东晓[1] 魏亚楠[1] 

机构地区：[1]华北电力大学经济与管理学院,北京市102206

出　　处：《电力系统自动化》2013年第3期54-57,共4页Automation of Electric Power Systems

基　　金：国家自然科学基金资助项目(71071052);中央高校基本科研业务费专项资金资助项目(12QX22)~~

摘　　要：提出一种有效的组合预测新模型进行电力负荷短期预测。不同预测模型在不同情况下的预测结果和精度有所变化,因此组合预测模型的权重应随着预测情景的变化而变化。文中将原始负荷数据分为训练集、验证集和测试集3类,并选择4种单一预测模型,即自回归滑动平均(ARMA)模型、广义自回归条件异方差(GARCH)模型、人工神经网络(ANN)和支持向量机(SVM)分别进行模型预测。对于需要预测的负荷,根据历史数据,将一年的数据先按照季度分类,再分别按照月、日、小时,利用模糊神经网络(FHNN)将其聚类。根据不同单一预测模型在不同情景下的误差计算出组合权重,从而获得组合预测模型。算例分析验证了所提出的组合预测模型的有效性和精确性。An efficient combinatorial modeling to forecast short-term power load is proposed.As the performance of individual models varies in different circumstances,the weights of the combinatorial forecast model should vary with the variation of circumstances.Here the historical power load is classified as three parts,the training set,validation set and test set and four models,namely,the autoregressive moving average（ARMA）,generalized autoregressive conditional heteroscedasticity（GRACH）,artificial neural network（ANN） and support vector machine（SVM）,are selected as candidate models.For short-term load forecast,the circumstance of the coming day is compared with those of past hours,past days,past months and past seasons,and then clustered into the same category by fuzzy Hopfield neural network（FHNN）.The combined weights are obtained according to mean absolute percentage errors of different models.Then the combinatorial forecast model with ARMA-GRACH-ANN-SVM weighted by FHNN clustering is obtained.A case study shows that the proposed combinatorial model outperforms other forecast methods.

关 键 词：负荷预测 组合预测 自回归滑动平均模型 广义自回归条件异方差模型 人工神经网络 支持向量机 模糊神经网络 相似日 

分 类 号：TM715[电气工程—电力系统及自动化]