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文章摘要
电力市场用电量需求分析预测模型研究
Research on power market electricity demand analysis and forecasting model
Received:July 24, 2016  Revised:July 24, 2016
DOI:
中文关键词: 预测  用电量需求  驱动因素  信用度评价  大数据平台
英文关键词: forecasting, electricity consumption demand, driving factor, credit rating, big data platform
基金项目:中国南方电网科技项目资助(GDKJ00000052).
Author NameAffiliationE-mail
Ding Yehao* Dongguan Power Supply Bureau of Guangdong Power Grid Co., Ltd. dingyehao_dggdj@163.com 
Mai Qi Dongguan Power Supply Bureau of Guangdong Power Grid Co., Ltd. chenglf_scut@163.com 
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中文摘要:
      结合广东省某市历史用电量数据,通过行业细分,设计了基于行业用电特性的电力市场用电量需求分析预测模型的总体架构,包括基于大数据平台的云计算技术架构设计,可有效解决“数据孤岛”的弊病。基于该架构和实例分析,讨论了几种常用的电量分析预测方法,包括行业驱动因素法、电力弹性系数法和电力相似月法等,给出了相应的预测过程和预测结果。然后,结合电量分析,进行了行业信用等级评价及景气指数分析模型的研究。最后,基于模型设计,探讨了电网-用户-售电商三方在市场竞争机制下的供需互动关系。所设计的模型可为市场营销服务策略的制定、电费回收、行业信用度评价及预警提供数理依据和模型,提高市场分析决策的能力。
英文摘要:
      Combined with historical electricity data of a city in Guangdong Province, and according to industrial subdivision, the overall framework of power market electricity demand analysis and forecasting model based on industrial electricity consumption feature was designed, including technical framework design of cloud computing based on big data platform, which can solve problems of data island. Based on the framework and case analysis, several commonly used electricity analysis and forecasting approaches were discussed, including the industrial driving factor method, electricity elasticity coefficient method and power similarity month method, the corresponding forecasting processes and results were given. Then, combined with electricity analysis, the industrial credit rating and climate index models were studied. Finally, based on study of the designed model, the supply-demand interaction relations of grid, user and power seller under market competitive mechanisms were discussed. The designed model can provide mathematical basis and model for marketing service strategies making, tariff recovery, industry credit rating and early-warning, accordingly the ability of market analysis and decision-making is improved.
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