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不同代际关系家庭的家电配置偏好度研究 被引量:1

Study on Household Appliance Configuration Preferences of Families with Various Generations
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摘要 家电配置偏好度的研究是对居民用户设计有效需求侧管理措施的基础。针对目前相关研究只考虑家电类型配置而忽略同种家电不同功率档位配置的问题,首先采用基于密度的DBSCAN(Density-based spatial clustering of applications with noise)聚类法对常用大功率电器实施功率聚类分档;进而根据各种代际类型家庭占比、各档位家电市场份额的统计信息建立最优化模型,对各类家庭对各家电档位的偏好度实施拟合分析;最后通过分枝、剪枝过程获得各类家庭常见家电配置方案及其偏好度。算例分析表明,所提方法能有效筛选出各类家庭的常见家电功率档位配置方案,且偏好度分析结果符合不同代际类型家庭在人口、居住面积上的特点。 The research on the preference of household appliance configuration schemes is the basis to design the effective demand side management(DSM)measures for residential consumers.In the present related research,only the appliance type configuration was considered while the different power configuration of the same household appliance was neglected.Firstly,density-based spatial clustering of applications with noise(DBSCAN)method is used to implement the power clustering of high-power electrical appliances.Then,based on the statistical information of the proportion of families with various generations,and the market share of the household appliances with different power configurations,the optimization model is established to carry out the fitting analysis on the preferences of power sizes of various household appliances.Finally,through branching and pruning process,the typical household appliance configuration schemes and their preferences are obtained.The analysis shows that the proposed method can effectively screen the typical configuration schemes of household appliances in various types of families,and the preference analysis result is in accordance with the characteristics of the families’size and living space with various generations.
作者 郑思源 韩跃峻 辛洁晴 ZHENG Siyuan;HAN Yuejun;XIN Jieqing(Department o{Electrical Engineering,Shanghai Jiaotong University,Shanghai 200240,China;Shibei Electricity Supply Company,SMEPC,Shanghai 200072,China)
出处 《现代电力》 北大核心 2018年第6期47-54,共8页 Modern Electric Power
基金 国家自然科学基金重点资助项目(51337005) 国家电网公司科技项目(5209141500QW)
关键词 电力需求侧管理 负荷分析 聚类 偏好度 代际关系 demand side management load analysis clustering preferences family generations
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