廖峰,徐聪颖,姚建刚,等.常德地区负荷特性及其影响因素分析[J].电网技术,2012,36(7):117-125.Liao Feng,Xu Congying,Yao Jiangang,et al.Load characteristics of Changde region and analysis on its influencing factors[J].Power System Technology,2012,36(7):117-125(in Chinese).
[3]
贺辉.电力负荷预测和负荷管理[M].北京:中国电力出版社,2013:1-4.
[4]
肖白,周潮,穆钢.空间电力负荷预测方法综述与展望[J].中国电机工程学报,2013,33(25):78-92.Xiao Bai,Zhou Chao,Mu Gang.Review and prospect of the spatial load forecasting methods[J].Proceedings of the CSEE,2013,33(25):78-92(in Chinese).
[5]
周建中,张亚超,李清清,等.基于动态自适应径向基函数网络的概率性短期负荷预测[J].电网技术,2010,34(3):37-41.Zhou Jianzhong,Zhang Yachao,Li Qingqing,et al.Probabilistic short-term load forecasting based on dynamic self-adaptive radial basis function network[J].Power System Technology,2010,34(3):37-41(in Chinese).
[6]
牛东晓,魏亚楠.基于FHNN相似日聚类自适应权重的短期电力负荷组合预测[J].电力系统自动化,2013,34(3):54-57.Niu Dongxiao,Wei Yannan.Short-term power load combined forecasting adaptive weighted by FHNN similar-day clustering[J].Automation of Electric Power Systems,2013,34(3):54-57(in Chinese).
[7]
李瑾,刘金朋,王建军.采用支持向量机和模拟退火算法的中长期负荷预测方法[J].中国电机工程学报,2011,31(16):63-66.Li Jin,Liu Jinpeng,Wang Jianjun.Mid-long term load forecasting based on simulated annealing and SVM algorithm[J].Proceedings of the CSEE,2011,31(16):63-66(in Chinese).
[8]
王大鹏,汪秉文.基于变权缓冲灰色模型的中长期负荷预测[J].电网技术,2013,37(1):167-171.Wang Dapeng,Wang Bingwen.Medium-and long-term load forecasting based on variable weights buffer grey model[J].Power System Technology,2013,37(1):167-171(in Chinese).
[9]
龙丹丽,黎静华,韦化.粗糙集法解多环境因素影响的母线负荷预测问题[J].电网技术,2013,37(5):1335-1340.Long Danli,Li Jinghua,Wei Hua.A solution of multi environmental factor-influenced bus load forecasting by rough set method[J].Power System Technology ,2013,37(5):1335-1340(in Chinese).
[10]
王保义,赵硕,张少敏.基于云计算和极限学习机的分布式电力负荷预测算法[J].电网技术,2014,38(2):526-531.Wang Baoyi,Zhao Shuo,Zhagn Shaomin.A distributed load forecasting algorithm based on cloud computing and extreme learning machine[J].Power System Technology,2014,38(2):526-531(in Chinese).
[11]
方仍存,周建中,张勇传,等.基于粒子群优化的非线性灰色Bernoulli模型在中长期负荷预测中的应用[J].电网技术,2008,32(12):60-63.Fang Rengcun,Zhou Jianzhong,Zhang Yongchuan,et al.Application of particle swarm optimization based nonlinear grey bernoulli model in medium and long-term load forecasting[J].Power System Technology,2008,32(12):60-63(in Chinese).
[12]
陈新宇,康重庆,陈敏杰.极值负荷及其出现时刻的概率化预测[J].中国电机工程学报,2011,31(22):64-72.Chen Xinyu,Kang Chongqing,Chen Minjie.Short term probabilistic forecasting of the magnitude and timing of extreme load[J].Proceedings of the CSEE,2011,31(22):64-72(in Chinese).
[13]
方仍存.电力系统负荷区间预测[D].武汉:华中科技大学,2008.
[14]
何耀耀,许启发,杨善林,等.基于RBF神经网络分位数回归的电力负荷概率密度预测方法[J].中国电机工程学报,2013,33(1):93-98.He Yaoyao,Xu Qifa,Yang Shanlin,et al.A power load probability density forecasting method based on RBF neural network quantile regression[J].Proceedings of the CSEE,2013,33(1):93-98(in Chinese).
[15]
Koenker R W,Bassett Jr G.Regression quantiles[J].Econometrica,1978,46(1):33-50.
[16]
许启发,蒋翠侠.分位数局部调整模型及应用[J].数量经济技术经济研究,2011,28(8):115-133.Xu Qifa,Jiang Cuixia.Quantile partial adjustment model and its application[J].Journal of Quantitative & Technical Economics,2011,28(8):115-133(in Chinese).
[17]
Taylor J W.A quantile regression neural network approach to estimating the conditional density of multiperiod returns[J].Journal of Forecasting,2000,19(4):299-311.
[18]
Feng Y,Li R,Sudjianto A,et al.Robust neural network with applications to credit portfolio data analysis[J].Statistics and Its Interface,2010,3(4):437.
[19]
Cannon A J.Quantile regression neural networks:implementation in R and application to precipitation downscaling[J].Computers & Geosciences,2010,37(9):1277-1284.
[20]
Portnoy S,Koenker R.Adaptive L-estimation for linear models[J].Annals of Statistics,1989,17(1):362-381.