Abstract：For big data feature of power load being more and more prominent, least absolute shrinkage and selection operator (Lasso) algorithm is introduced to solve the power load problem of big data by extracting features of high-dimensional data from power load and related weather factors. To avoid the adverse effect that the prediction accuracy of RBF neural network is affected severely, caused by that the autocorrelation of input space is too serious and the dimension of network is too high, the improved RBF neural network power load forecasting model is proposed based on principal component analysis. It eliminates the correlation among weather factors, excludes redundancy and extracts feature variables of multiple weather factors.The obtained weather characteristics are taken as the modeling objects of the RBF network together with the dates of historical load, which not only characterize fully the impact of weather factors on the power load, but also simplify the prediction model and accelerate the forecasting rate. Through the experiments of forecasting and analyzing to the actual power system load in a certain region of southern United States, it proved the validity and reliability of the method fully.
张淑清,任爽,陈荣飞,钱磊,姜万录,李盼. 基于大数据简约及PCA改进RBF网络的短期电力负荷预测[J]. 计量学报, 2018, 39(3): 392-396.
ZHANG Shu-qing,REN Shuang,CHEN Rong-fei,QIAN Lei,JIANG Wan-lu,LI Pan. Short-term Power Load Forecast Based on Big Data Reduction and PCA-improved RBF Network. Acta Metrologica Sinica, 2018, 39(3): 392-396.
［1］钟清,孙闻,余南华,等.主动配电网规划中的负荷预测与发电预测［J］. 中国电机工程学报,2014,34(19):3050-3056.
［2］宋亚奇,周国亮,朱永利.智能电网大数据处理技术现状与挑战［J］.电网技术,2013,37(4):927-935.
［3］王金甲,陈春,洪文学.基于序lasso的时间序列分析方法［J］.燕山大学学报,2015,39(1):30-34.
［4］Hor C L, Wajithia S. Analyzing the impact of weather variables on monthly electricity demand［J］.IEEE Transactions on Power Systems,2005,20(4):2078-2085.
［5］张淑清,赵朋程,陈颖,等.基于EEMD和关联维数的小电流接地故障选线［J］.计量学报,2016,37(3): 300-305.
［6］Lei S L, Sun C X, Zhou Q, et al. Short-term load forecasting method based on RBF neural network and ANFIS system［J］.Proceedings of the CSEE,2005, 25(22):78-82.
［7］李冬辉,王乐英,李晟.基于PCA的空调系统传感器故障诊断［J］.电工技术学报,2008,23(6):130-136.
［8］刘荣,方鸽飞.改进Elman神经网络的综合气象短期负荷预测［J］.电力系统保护与控制,2012,40(22): 113-117.
［9］周松林,茆美琴,苏建徽.基于主元分析与人工神经网络的风电功率预测［J］.电网技术,2011,35:128-132.
［10］王惠新,王玉田,黄满义,等.基于双树复小波及递归图的管道泄漏定位方法［J］.计量学报,2015 ,36 (6):617-621.
［11］贾明兴,赵春晖,王福利,等.基于RBF神经网络的非线性主元分析方法［J］.仪器仪表学报,2008,29(3): 453-457.
［12］毛李帆,姚建刚,金永顺,等.中长期电力组合预测模型的理论研究［J］.中国电机工程学报,2010,30(16):53-59.
［13］张淑清,董璇,翟欣沛,等.基于EEMD和混沌的信号特征提取方法及应用J］. 计量学报,2013,34(2):173-179.
［14］何群,赵文爽,江国乾,等.基于EEMD与AR建模的风电场风速预测［J］. 计量学报,2015, 36(2):181-186.
［15］张淑清,任爽,姜安琦,等. PCA-GRNN在综合气象短期负荷预测中的应用［J］. 计量学报,2017, 38(3): 340-344.
［16］焦润海,苏辰隽,林碧英,等.基于气象信息因素修正的灰色短期负荷预测模型［J］.电网技术,2013,37(3):720-725.
2018, Vol. 39 
