[1] |
ROBU V , VINYALS M , ROGERS A ,et al. Efficient buyer groups for prediction-of-use electricity tariffs[C]// AAAI Conference on Artificial Intelligence,July 27-31,2014,Québec City,Québec,Canada. Palo Alto:AIII Press, 2014: 451-457.
|
[2] |
PEPERMANS G , WILLEMS B . Cost recovery in congested electricity networks[J]. Zeitschrift Für Energiewirtschaft, 2010,34(3): 195-208.
|
[3] |
张晓峰 . 电力大客户电费回收风险防范体系的构建[J]. 内蒙古科技与经济, 2013(24): 121-123.
|
|
ZHANG X F . Construction of risk prevention system for electric power customer's electricity recovery[J]. Inner Mongolia Science Technology & Economy, 2013(24): 121-123.
|
[4] |
裘华东, 涂莹, 丁麒 . 基于标签库系统的电力企业客户画像构建与信用评估及电费风险防控应用[J]. 电信科学, 2017,33(1): 214-221.
|
|
QIU H D , TU Y , DING Q . Construction of power customer portrait and its credit evaluation and electricity fee risk control based on tag library system[J]. Telecommunications Science, 2017,33(Z1): 214-221.
|
[5] |
HUANG Y , YAN Y . Research of evaluating credit-risk in power enterprise based on SVM and VIKOR method[C]// IEEE International Conference on Industrial Engineering and Engineering Management,Dec 8-11,2009,Singapore. Piscataway:IEEE Press, 2009: 1596-1599.
|
[6] |
刘金 . 基于改进层次分析法的电力客户欠费风险评价[J]. 电子世界, 2014(19): 182-183.
|
|
LIU J . Risk evaluation of electric power customer arrears based on improved analytic hierarchy process[J]. Electronic World, 2014(19): 182-183.
|
[7] |
WIGAN M R , CLARKE R . Big data’s big unintended consequences[J]. Computer, 2013,46(6): 46-53.
|
[8] |
RAHMAN M N , ESMAILPOUR A , ZHAO J . Machine learning with big data an efficient electricity generation forecasting system[J]. Big Data Research, 2016(5): 9-15.
|
[9] |
杨华飞, 李栋华, 程明 . 电力大数据关键技术及建设思路的分析和研究[J]. 电力信息与通信技术, 2015,13(1): 7-10.
|
|
YANG H F , LI D H , CHENG M . Analysis and research of key technologies and construction ideas of power big data[J]. Electric Power Information and Communication Technology, 2015,13(1): 7-10.
|
[10] |
赵永良, 秦萱, 吴尚远 ,等. 基于数据挖掘的高压用户电费回收风险预测[J]. 电力信息与通信技术, 2015,13(9): 57-61.
|
|
ZHAO Y L , QIN X , WU S Y ,et al. Electricity recovery risk prediction of high-voltage customers based on data mining[J]. Electric Power Information and Communication Technology, 2015,13(9): 57-61.
|
[11] |
黄文思, 郝悍勇, 李金湖 ,等. 基于决策树算法的电力客户欠费风险预测[J]. 电力信息与通信技术, 2016(1): 19-22.
|
|
HUANG W S , HAO H Y , LI J H ,et al. Prediction of power customer arrear risk based on decision tree algorithm[J]. Electric Power Information and Communication Technology, 2016(1): 19-22.
|
[12] |
吴漾, 朱州 . 基于特征选择改进LR-Bagging算法的电力欠费风险居民客户预测[J]. 电子产品世界, 2017(4): 70-75.
|
|
WU Y , ZHU Z . The arrears risk prediction of power residential customers based on LR-Bagging algorithm improved by feature selection[J]. Electronic Engineering and Products World, 2017(4): 70-75.
|
[13] |
张禄, 潘鸣宇, 田贺平 ,等. 基于数据挖掘技术的电力客户欠费风险预警研究[J]. 2017.
|
|
ZHANG L , PAN M Y , TIAN H P ,et al. Risk early-warning of electricity customers’arrears based on data mining technology[J]. 2017.
|
[14] |
GRANELL R , AXON C J , WALLOM D C H . Predicting winning and losing businesses when changing electricity tariffs[J]. Applied Energy, 2014,133(10): 298-307.
|
[15] |
KAMILARIS A , KALLURI B , KONDEPUDI S ,et al. A literature survey on measuring energy usage for miscellaneous electric loads in offices and commercial buildings[J]. Renewable &Sustainable Energy Reviews, 2014(34): 536-550.
|
[16] |
GRANELL R , AXON C J , WALLOM D C H ,et al. Power-use profile analysis of non-domestic consumers for electricity tariff switching[J]. Energy Efficiency, 2016,9(3): 825-841.
|
[17] |
FELDMAN R , SANGER J . Text mining handbook:advanced approaches in analyzing unstructured data[M]. Oxford City: Cambridge University PressPress, 2006.
|