CAJ | 학술논문

对智能小区的居民用电行为展开研究,基于云计算平台和并行k-means聚类算法,建立了峰时耗电率、负荷率、谷电系数等时间序列特征,并采用熵权法计算各类特征权重,实验数据来自已建的智能小区中的600名用户.实验结果表明,智能小区的居民用户被分成空置房、上班族、上班族+老人、老人家庭、商业用户等5类用户,聚类的准确率达到了91.2%,证明文中基于云计算平台和并行 k_means聚类算法的居民用电行为分析模型是有效的.
대지능소구적거민용전행위전개연구,기우운계산평태화병행k-means취류산법,건립료봉시모전솔、부하솔、곡전계수등시간서렬특정,병채용적권법계산각류특정권중,실험수거래자이건적지능소구중적600명용호.실험결과표명,지능소구적거민용호피분성공치방、상반족、상반족+노인、노인가정、상업용호등5류용호,취류적준학솔체도료91.2%,증명문중기우운계산평태화병행 k_means취류산법적거민용전행위분석모형시유효적.
To research residential electricity consumption behavior in intelligent residential area, based on cloud computing platform and parallel k-means clustering algorithm the time series features such as electricity consumption rate during peak hour, load rate, valley load coefficient, namely the ratio of electricity consumption during valley hour to total electricity consumption, and so on are established and the weights of various features are calculated by entropy weight method. Experimental data is from 600 users living in a certain built smart community. Experimental results show that the residential users in the smart community are divided into five categories, i.e., vacant dwellings, office staff, office staff living with elders, aged families and commercial customer, and the clustering accuracy reaches 91.2%, and thus it is proved that the proposed model for residential electricity consumption behavior analysis is correct and effective.