利用居民用电量的住房面积预测算法设计
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摘要
从供电企业的角度出发,为了向用户提供相似邻里用电比较服务,引导居民节约用电,提出了利用居民用电量的住房面积预测算法,在对居民用电量和住房面积进行数据清洗的基础上,分别以年制冷与取暖电量、年基本生活电量以及最近12个月有效月电量为建模特征量,借助支持向量回归、神经网络、K-means聚类等算法工具,构建了4种模型,比较并验证了模型的效果,其中结合K-means聚类的神经网络模型预测效果最好,平均预测偏差为19.755%。结果表明,该算法能通过居民用电量对住房面积进行较准确的预测,为相似邻里用电比较服务提供重要支持。
From the perspective of power supply companies, in order to provide users with similar neighborhood electricity usage comparison services for guiding residents to save electricity, a housing area prediction algorithm based on residential electricity consumption is proposed. On the basis of data cleaning of residential electricity consumption and housing area, using the annual cooling and heating power, annual basic living power and the effective monthly electricity consumption in the last 12 months as modeling features, four models are built with support vector regression, neural network,K-means clustering and other algorithm tools and the effect of models are compared and verified. The neural network model combined with K-means clustering has the best prediction effect, and the average prediction deviation is 19.755%. The results show that this algorithm can accurately predict the housing area through residential electricity consumption, and provide important support for similar neighborhood electricity usage comparison services.
From the perspective of power supply companies, in order to provide users with similar neighborhood electricity usage comparison services for guiding residents to save electricity, a housing area prediction algorithm based on residential electricity consumption is proposed. On the basis of data cleaning of residential electricity consumption and housing area, using the annual cooling and heating power, annual basic living power and the effective monthly electricity consumption in the last 12 months as modeling features, four models are built with support vector regression, neural network,K-means clustering and other algorithm tools and the effect of models are compared and verified. The neural network model combined with K-means clustering has the best prediction effect, and the average prediction deviation is 19.755%. The results show that this algorithm can accurately predict the housing area through residential electricity consumption, and provide important support for similar neighborhood electricity usage comparison services.
引文
[1]焦广旭,郑喜山,卫婧菲.家庭节约用电重要性及方法探讨[J].科技资讯,2014(5):239-240.
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[16]周志华.机器学习[M].北京:清华大学出版社,2016:97-107.
[17]高大启.有教师的线性基本函数前向三层神经网络结构研究[J].电路与系统学报,1997,21(3):31-37.GAO Daqi.On structures of supervised linear basis function feedforward three-layered neural networks[J].Journal of Circuits and Systems,1997,21(3):31-37.
[18]姜园,张朝阳,仇佩亮,等.用于数据挖掘的聚类算法[J].电子与信息学报,2005,27(4):655-662.JIANG Yuan,ZHANG Chaoyang,QIU Peiliang,et al.Clustering algorithms used in data mining[J].Journal of Electronics&Information,2005,27(4):655-662.
[2]贾君君.居民部门节能和碳减排:消费者行为、能效措施和政策工具[D].合肥:中国科学技术大学,2018.
[3]岳婷.城市居民节能行为影响因素及引导政策研究[D].徐州:中国矿业大学,2014.
[4]DARBY S.The effectiveness of feedback on energy consumption:a review of the literature on metering,billing and direct displays[R].Environmental Change Institute,University of Oxford,2006.
[5]ABRAHAMSE W,STEG L,VLEK C,et al.The effect of tailored information,goal setting,and tailored feedback on household energy use,energy-related behaviours,and behavioral antecedents[J].Journal of Environmental Psychology,2007,27(4):265-276.
[6]MYEM B J.Re-materialising energy use through transparent monitoring systems[J].Energy Policy,2008,36(12):4454-4459.
[7]FARUQUI A,SERGICI S,SHARIF A.The impact of informational feedback on energy consumption-A survey of the experimental evidence[J].Energy,2010,35(4):1598-1608.
[8]HARGREAVES T,NYE M,BURGESS J.Making energy visible:A qualitative field study of how householders interact with feedback from smart energy monitors[J].Energy policy,2010,38(10):6111-6119.
[9]孙禹,冷红,蒋存妍.基于人行为影响的住区建筑多主体集成能耗模型[J].土木建筑与环境工程,2017,39(1):38-50.SUN Yu,LENG Hong,JIANG Cunyan.Multi-agent based energy model for domestic duildings based on occupant behavior[J].Journal of Civil,Architectural&Environmental Engineering,2017,39(1):38-50.
[10]蔡伟光.中国建筑能耗影响因素分析模型与实证研究[D].重庆:重庆大学,2011.
[11]李楠.夏热冬冷地区人员行为对住宅建筑能耗的影响研究[D].重庆:重庆大学,2011.
[12]江樱,王志强,戴波.基于大数据的居民用电消费习惯研究与分析[J].电力信息与通信技术,2015,13(11):7-11.JIANG Ying,WANG Zhiqiang,DAI Bo.Research and analysis of residential electricity consumption behavior based on big data[J].Electric Power Information and Communication Technology,2015,13(11):7-11.
[13]BAESENS B.大数据分析:数据科学应用场景与实践精髓[M].柯晓燕,张纪元,译.北京:人民邮电出版社,2016:20-24.
[14]冼广铭,曾碧卿.ε-支持向量回归机算法及其应用[J].计算机工程与应用,2008,44(17):40-42.XIAN Guangming,ZENG Biqing.ε-SVR algorithm and its application[J].Computer Engineering and Applications,2008,44(17):40-42.
[15]肖建,于龙,白裔峰.支持向量回归中核函数和超参数选择方法综述[J].西南交通大学学报,2008,43(3):297-303.XIAO Jian,YU Long,BAI Yifeng.Survey of the selection of kernels and hyper-parameters in support vector regression[J].Journal of Southwest Jiaotong University,2008,43(3):297-303.
[16]周志华.机器学习[M].北京:清华大学出版社,2016:97-107.
[17]高大启.有教师的线性基本函数前向三层神经网络结构研究[J].电路与系统学报,1997,21(3):31-37.GAO Daqi.On structures of supervised linear basis function feedforward three-layered neural networks[J].Journal of Circuits and Systems,1997,21(3):31-37.
[18]姜园,张朝阳,仇佩亮,等.用于数据挖掘的聚类算法[J].电子与信息学报,2005,27(4):655-662.JIANG Yuan,ZHANG Chaoyang,QIU Peiliang,et al.Clustering algorithms used in data mining[J].Journal of Electronics&Information,2005,27(4):655-662.