| 丛小涵,苏慧玲,李海思,王蓓蓓.基于数据挖掘与需求响应的个性化智能用电套餐研究[J].电力需求侧管理,2019,21(5):21-25 |
| 基于数据挖掘与需求响应的个性化智能用电套餐研究 |
| Research on intelligent electricity package based on deep mining and demand response |
| 投稿时间：2019-01-25 修订日期：2019-04-14 |
| DOI：DOI:10.3969/j.issn.1009-1831.2019.05.005 |
| 中文关键词: 自编码神经网络 消费者心理学 峰谷分时电价 叠加电价 |
| 英文关键词: self⁃coding neural network customer psychology time⁃of⁃use price superimposed price |
| 基金项目:国家自然科学基金项目（71471036）；国家电网公司科技项目（SGTYHT/16-JS-198） |
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| 摘要点击次数: 2011 |
| 全文下载次数: 869 |
| 中文摘要: |
| 在电力体制改革的背景下，有必要精细化挖掘用户用电特性，同时考虑售电商偏差考核控制的问题，制定套餐优化需求响应策略。首先基于自编码神经网络和模糊C均值聚类的方法对用户用电曲线进行模式分类，然后基于消费者心理学用户响应模型，对用户不同用电模式建立峰谷分时电价优化模型，在此基础上，对不同用电模式建立峰平时段叠加电价模型。研究表明，套餐制定可以有效引导用户调整用电行为，降低用电模式间差异，从偏差考核的角度看，有助于制定月购电策略。 |
| 英文摘要: |
| Under the background of power system reform, it is necessary to mine the user electricity characteristic and develop corresponding packages to optimize the demand response strategy considering the problem of the deviation assessment of load server entities. Firstly, the user’s power consumption curve based on selfcoding neural network and fuzzy C means clustering method is classified. Then an optimization model of time of use electricity price based on the user response model of consumer psychology is established. Furtherly, a superimposed electricity price model for the peak period is established. Research shows that the development of package can effectively guide users to adjust their electricity consumption behavior, so as to reduce the difference between electricity consumption modes. From the perspective of deviation assessment, it is helpful to develop monthly electricity purchase strategy. |
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