肖白;赵晓宁;姜卓;施永刚;焦明曦;王徭;

• 1:东北电力大学电气工程学院
• 2:北华大学计算机科学技术学院
• 3:国网吉林省电力有限公司通化供电公司
• 4:国网吉林省电力有限公司长春供电公司

摘要(Abstract)：

若直接使用实测负荷数据最大值进行空间负荷预测,则元胞负荷中的异常数据会导致预测结果精度降低,考虑到通过确定并利用元胞负荷合理最大值可以明显改善预测精度,提出一种基于模糊信息粒化与支持向量机的空间负荷预测方法。首先构建电力地理信息系统,并在其中生成2类元胞。其次按照时间尺度的长短区分Ⅰ类元胞负荷颗粒度的粗细,通过划分模糊粒化窗口,建立合理的模糊集对Ⅰ类元胞细颗粒度下的历史负荷数据进行模糊信息粒化,进而确定出Ⅰ类元胞粗颗粒度下的历史负荷的合理最大值。然后采用支持向量机模型,对粗颗粒度下的Ⅰ类元胞负荷进行预测。最后确定Ⅰ类元胞负荷密度均衡系数,求取分类负荷密度指标,结合用地信息求得各Ⅱ类元胞负荷预测值,从而实现对空间电力负荷预测结果的网格化。工程实例表明了该方法的实用性和有效性。

关键词(KeyWords)： 空间负荷预测;地理信息系统;模糊信息粒化;支持向量机;网格化

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金项目(51177009);; 吉林省产业创新专项基金项目(2019C058-7);; 吉林省教育厅科技项目(JJKH20180442KJ)~~

作者(Author): 肖白;赵晓宁;姜卓;施永刚;焦明曦;王徭;

Email:

DOI: 10.13335/j.1000-3673.pst.2019.1463

参考文献(References)：

• [1]Willis H L.Spatial electric load forecasting[M].New York,USA:Marcel Dekker,2002:217-243．
• [2]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)．
• [3]Xie Jingrui,Hong Tao.Load forecasting using 24 solar terms[J].Journal of Modern Power Systems and Clean Energy,2018,6(2):208-214．
• [4]Hong Tao,Pierre Pinson,Fan Shu.Global energy forecasting competition 2012[J].International Journal of Forecasting,2014,30(2):357-363．
• [5]Luo Jian,Hong Tao,Yue Meng.Real-time anomaly detection for very short-term load forecasting[J].Journal of Modern Power Systems and Clean Energy,2018,6(2):235-243．
• [6]杨丽徙，王金凤，陈根永，等．基于元胞自动机理论的电力负荷空间分布预测[J]．中国电机工程学报，2007,27(4):15-20.Yang Lixi,Wang Jinfeng,Chen Genyong,et al.Load spatial distribution forecasting model on cellular automata theory[J].Proceedings of the CSEE,2007,27(4):15-20(in Chinese)．
• [7]肖白，黎平．城网空间电力负荷预测中的规律性分析[J]．电网技术，2009,33(20):113-118.Xiao Bai,Li Ping.A load regularity analysis on spatial load forecasting of urban power system[J].Power System Technology,2009,33(20):113-118(in Chinese)．
• [8]肖白，刘庆永，牛强，等．基于元胞负荷特性分析的RBF神经网络空间负荷预测方法[J]．电网技术，2018,42(1):301-306.Xiao Bai,Liu Qingyong,Niu Qiang,et al.Cellular load characteristics analysis RBF neural network spatial load forecasting method[J].Power System Technology,2018,42(1):301-306(in Chinese)．
• [9]乐欢，王主丁，肖栋柱，等．基于空区推论的空间负荷预测分类分区实用法[J]．电力系统自动化，2009,33(7):81-85.Le Huan,Wang Zhuding,Xiao Dongzhu,et al.Vacant area inference based classification and subarea practical method for spatial load forecasting[J].Automation of Electric Power Systems,2009,33(7):81-85(in Chinese)．
• [10]朱凤娟，王主丁，陆俭，等．考虑小区发展不均衡的空间负荷预测分类分区法[J]．电力系统自动化，2012,36(12):41-48.Zhu Fengjuan,Wang Zhuding,Lu Jian,et al.Disequilibrium development areas based classification and subarea method for spatial load forecasting[J].Automation of Electric Power Systems,2012,36(12):41-48(in Chinese)．
• [11]刘思，傅旭华，叶承晋，等．应用聚类分析与非参数核密度估计的空间负荷分布规律[J]．电网技术，2017,41(2):604-610.Liu Si,Fu Xuhua,Ye Chengjin,et al.Spatial load distribution based on clustering analysis and non-parametric kernel density estimation[J].Power System Technology,2017,41(2):604-610(in Chinese)．
• [12]肖白，杨修宇，穆钢．基于元胞历史负荷数据的负荷密度指标法[J]．电网技术，2014,38(4):1014-1019.Xiao Bai,Yang Xiuyu,Mu Gang.A load density index method based on historical data of cell load[J].Power System Technology,2014,38(4):1014-1019(in Chinese)．
• [13]肖白，刘庆永，房龙江，等．基于模糊粗糙集理论和时空信息的空间负荷预测[J]．电力建设，2017,38(1):58-67.Xiao Bai,Liu Qingyong,Fang Longjiang,et al.Spatial load forecasting based on fuzzy rough set theory with spatial and temporal information[J].Electric Power Construction,2017,38(1):58-67(in Chinese)．
• [14]肖白，杨欣桐，田莉，等．计及元胞发展程度的空间负荷预测方法[J]．电力系统自动化，2018,42(1):61-66.Xiao Bai,Yang Xintong,Tian Li,et al.Spatial load forecasting method considering the development degree of the cells[J].Automation of Electric Power Systems,2018,42(1):61-66(in Chinese)．
• [15]Witold Pedrycz.Knowledge-based clustering-from data to information granules[M].New York:John Wiley&Sons,Inc,2003．
• [16]Bargicla A,Pedrycz W.Granular computing:an introduction[M].Dodrecht:Kluwer Academic Publishers,2003．
• [17]许宗礼．人民币兑美元汇率短期预测研究-基于模糊信息粒化的SVM模型[D]．广州：暨南大学，2016．
• [18]候聪，何大四，靳晓东．基于模糊信息粒化和支持向量机的空调负荷预测[J]．建筑热能通风空调，2017,36(2):28-32.Hou Cong,He Dasi,Jin Xiaodong.Air conditioning load forecasting based on fuzzy information granulation and support vector machine[J].Building Energy&Environment,2017,36(2):28-32(in Chinese)．
• [19]王恺，关少卿，汪令祥，等．基于模糊信息粒化和最小二乘支持向量机的风电功率联合预测建模[J]．电力系统保护与控制，2015,43(2):27-31.Wang Kai,Guan Shaoqing,Wang Lingxiang,et al.Fuzzy information granulation and least squares support vector machine joint forecasting modeling of wind power[J].Power System Protection and Control,2015,43(2):27-31(in Chinese)．
• [20]丁志勇，杨苹，杨曦，等．基于连续时间段聚类的支持向量机风电功率预测方法[J]．电力系统自动化，2012,36(14):132-135.Ding Zhiyong,Yang Ping,Yang Xi,et al.Wind power prediction method based on sequential time clustering support vector machine[J].Automation of Electric Power Systems,2012,36(14):132-135(in Chinese)．
• [21]肖白，聂鹏，穆钢，等．基于多级聚类分析和支持向量机的空间负荷预测方法[J]．电力系统自动化，2015,39(12):56-60.Xiao Bai,Nie Peng,Mu Gang,et al.A spatial load forecasting method based on multilevel clustering analysis and support vector machine[J].Automation of Electric Power Systems,2015,39(12):56-60(in Chinese)．
• [22]杨茂，陈新鑫，张强，等．基于支持向量机的短期风速预测研究综述[J]．东北电力大学学报，2017,37(4):1-7.Yang Mao,Chen Xinxin,Zhang Qiang,et al.A review of short-term wind speed prediction based on support vector machine[J].Journal of Northeast Electric Power University,2017,37(4):1-7(in Chinese)．
• [23]李国庆，张钰，张明江，等．基于MRMR的集合经验模态分解和支持向量机的风电功率实时预测[J]．东北电力大学学报，2017,37(2):39-44.Li Guoqing,Zhang Yu,Zhang Mingjiang,et al.The wind power real-time prediction based on the EEMD and SVM of the MRMR[J]Journal of Northeast Electric Power University,2017,37(2):39-44(in Chinese)．
• [24]肖白，蒲睿，穆钢．基于多尺度空间分辨率的空间负荷预测误差评价方法[J]．中国电机工程学报，2015,35(22):5731-5739.Xiao Bai,Pu Rui,Mu Gang.Method of spatial load forecasting error evaluation based on the multi-scale spatial resolution[J].Proceedings of the CSEE,2015,35(22):5731-5739(in Chinese)．
• [25]Prada J,Dorronsoro J R.General noise support vector regression with non-constant uncertainty intervals for solar radiation prediction[J].Journal of Modern Power Systems and Clean Energy,2018,6(2):268-280．
• [26]Ma Ziming,Zhong Haiwang,Le Xie,et al.Month ahead average daily electricity price profile forecasting based on a hybrid nonlinear regression and SVM model:an ERCOT case study[J].Journal of Modern Power Systems and Clean Energy,2018,6(2):281-291．
• [27]吴倩红，高军，候广松，等．实现影响因素多源异构融合的短期负荷预测支持向量机算法[J]．电力系统自动化，2016,40(15):67-71.Wu Qianhong,Gao Jun,Hou Guangsong,et al.Impact of renewable power plant collector system on stability of grid-connected multi-converters[J].Automation of Electric Power Systems,2016,40(15):67-71(in Chinese)．
• [28]崔天晓，周小龙，刘文浩，等．基于Hilbert包络谱和SVM的齿轮故障诊断[J]．东北电力大学学报，2017,37(6):56-61.Cui Tianxiao,Zhou Xiaolong,Liu Wenhao,et al.Gear fault diagnosis based on Hilbert envelope spectrum and SVM[J].Journal of Northeast Electric Power University,2017,37(6):56-61(in Chinese)．
• [29]杨茂，黄宾阳，江博，等．基于卡尔曼滤波和支持向量机的风电功率实时预测研究[J]．东北电力大学学报，2017,37(2):45-51.Yang Mao,Huang Binyang,Jiang Bo,et al.Real-time prediction for wind power based on Kalman filter and support vector machines[J].Journal of Northeast Electric Power University,2017,37(2):45-51(in Chinese)．
• [30]康重庆，夏清，刘梅．电力系统负荷预测[M]．北京：中国电力出版社，2007:94-97.