[1] |
宗柳,李扬,王蓓蓓. 计及需求响应的多维度用电特征精细挖掘 [J]. 电力系统自动化,2012,36(20):54-58.
ZONGL,LIY,WANGB B. Fine-mining of multi-dimension electrical characteristics considering demand response [J]. Automation of Electric Power Systems,2012,36(20):54-58. |
[2] |
钱程. 基于用户用电行为建模和参数辨识的需求响应应用研究 [D]. 南京:东南大学,2016.
QIANC. Research on the application of demand response based on modeling of electrical behavior and parameter identification [D]. Nanjing:Southeast University,2016. |
[3] |
KWACJ,FLORAJ,RAJAGOPALR. Household energy consumption segmentation using hourly data [J]. IEEE Transactions on Smart Grid,2014,5(1):420-430. |
[4] |
周谢. 电力负荷特性指标及其内在关联性分析 [D]. 长沙:长沙理工大学,2013.
ZHOUX. The analysis on power load characteristics index and its intrinsic correlation relationship [D]. Changsha:Changsha University of Science&Technology,2013. |
[5] |
刘国辉,赵佳,孙毅. 基于模糊优化集对分析理论的需求响应潜力评估 [J]. 电力需求侧管理,2018,20(6):1-5.
LIUG H,ZHAOJ,SUNY. Potential evaluation of demand response based on fuzzy optimization of set pair analysis [J]. power demand side management,2018,20(6):1-5. |
[6] |
李亚平,王珂,郭晓蕊,等. 基于多场景评估的区域电网需求响应潜力 [J]. 电网与清洁能源,2015,31(7):1-7.
LIY P,WANGK,GUOX R,et al. Demand response potential based on multi-scenarios assessment in regional power system [J]. power system and clean energy,2015,31(7):1-7. |
[7] |
任炳俐,张振高,王学军,等. 基于用电采集数据的需求响应削峰潜力评估方法 [J]. 电力建设,2016,37(11):64-70.
RENB L,ZHANGZ G,WANGX J,et al. Assessment method of demand response peak shaving potential based on metered load data [J]. Electric Power Construction,2016,37(11):64-70. |
[8] |
孙彦萍,李虹,杨文海,等. 基于SOM需求响应潜力的居民用户优化聚合模型 [J]. 电力建设,2017,38(7):25-33.
SUNY P,LIH,YANGW H,et al. Optimized aggregation model for resident users based on SOM demand response potential [J]. Electric Power Construction,2017,38(7):25-33. |
[9] |
王蓓蓓,朱峰,嵇文路,等. 中央空调降负荷潜力建模及影响因素分析 [J]. 电力系统自动化,2016,40(19):44-52.
WANGB B,ZHUF,JIW L,et al. Load cutting potential modeling of central air-conditioning and analysis on influencing factors [J]. Automation of Electric Power Systems,2016,40(19):44-52. |
[10] |
CHUANL,UKILA. Modeling and validation of electrical load profiling in residential buildings in Singapore [J]. IEEE Transactions on Power Systems,2015,30(5):2800-2809. |
[11] |
ALSTONEP,POTTERJ,PIETTEM A,et al. California demand response potential study:charting California's demand response future [R]. Sacramento: California Public Utilities Commission,2016. |
[12] |
STARKEM,ALKADIN,MAO. Assessment of industrial load for demand response across US regions of the western interconnect [R]. Oak Ridge,TN:Oak Ridge National Lab. 2013. |
[13] |
ALSTONEP,POTTERJ,PIETTEM,et al. 2025 California demand response potential study final report on phase [R]. Berkeley: Lawrence Berkeley National Laboratory,2017. |
[14] |
Mac KayD J C. Information theory,inference and learning algorithms [M]. Cambridge: Cambridge University Press,2003. |
[15] |
CHICCOG.Overview and performance assessment of the clustering methods for electrical load pattern grouping [J]. Energy,2012,42(1):68-80. |
[16] |
国家市场监督管理总局. 国民经济行业分类:GB/T 4754—2017 [S].北京:国家统计局,2017.
State Administration of Market Supervision and Administration. National economy industry classification :GB/T 4754—2017 [S]. Beijing: National Bureau of Statistics,2017. |
[17] |
U.S. EIA. Commercial buildings energy consumption survey:trends in lighting in commercial buildings [EB/OL]. (2017-05-17) [2020-06-30]. https://www.eia.gov/consumption/commercial/reports/2012/lighting/. |
[18] |
LODGINGS. Here's where hotels spend the most on energy [EB/OL]. (2018-04-24) [2020-06-30]. https://lodgingmagazine.com/where-hotels-spend-most-energy. |
[19] |
LEE M,ASLAMO,FOSTERB,et al. Assessment of demand response and advanced metering [R]. Washington D.C.: Federal Energy Regulatory Commission,Tech. Rep., 2018. |
[20] |
张琳,许可,黄耀,等. 基于模糊聚类的园区配网峰谷特性和潜力分析研究 [J]. 南方能源建设,2020,7(1):27-32.
ZHANGL,XUK,HUANGY,et al. Analysis of potential shifting and filling potential of campus distribution network based on fuzzy clustering [J]. South Energy Construction,2020,7(1):27-32. |