低抽样数据的极限学习能源解析算法（英文）  

Extreme Learning Machine for Energy Disaggregation Using Low Sampling Consumption Data

作　　者：唐秀明[1,2] 袁荣湘[1] 陈君[1,2] 

机构地区：[1]武汉大学电气工程学院,武汉430072 [2]湖南科技大学信息与电气工程学院,湘潭411201

出　　处：《电气工程学报》2016年第3期34-40,共7页Journal of Electrical Engineering

基　　金：Supported by National Natural Science Funds(61273169,61134006,61105080);the Scientific Research Fund of Hunan Provincial Education Department(13A016);the Science and Technology Planning Project of Xiangtan City Hunan Province(NY20141006);Hunan Provincial Natural Science Foundation of China(11JJ4057,14JJ2099)

摘　　要：能量解析在分解综合负荷及提高设备的能量效率方面起到重要作用。当前,能量解析方法主要存在较低准确性和效率问题。论文提出一种基于低频监控数据的多输出极限学习的能源解析方法。该方法的特征映射函数可一次随机生成且无需调整其参数,与支持向量机方法相比,其优化目标函数具有较少的优化约束条件且更易实现。用实际记录的房屋能量数据进行仿真,仿真结果表明:与支持向量机相比,本文方法具有更高的训练速度和分类精度、更少的计算时间和更强的泛化能力。Energy disaggregation plays an important role in the rational use and management of energy within a whole building. However, the current energy disaggregation methods encounter the problems of low accuracy to classify and long computational time to choose appropriate parameters. To solve this difficulty, this paper proposes a novel energy disaggregation method based multi-output extreme learning machine（MO-ELM） to analysis low-frequency monitoring data gathered by meters distributed in a building. The MO-ELM whose parameters of feature mapping need not be tuned and can be fixed once randomly generated requires fewer optimization constraints with the objective function and results in simpler implementation compared to SVM. The evaluation results using a dataset of power traces collected in real-world home setting shows that proposed method have a satisfied classification accuracy, training speed and generalization performance and less computational time.

关 键 词：能量解析 监督分类 极限学习 

分 类 号：TM301[电气工程—电机]