Non-intrusive energy estimation using random forest based multi-label classification and integer linear programming
Non-intrusive energy estimation using random forest based multi-label classification and integer linear programming
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使用基于随机森林的多标签分类和整数线性规划的非侵入式能量估计
DOI：
10.1016/j.egyr.2021.08.045
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发表时间：
2021-11
期刊：
影响因子：
5.2
通讯作者：
Huang Xueliang
中科院分区：
文献类型：
--
作者：
Liu Yu;Liu Congxiao;Shen Yiwen;Zhao Xin;Gao Shan;Huang Xueliang
家庭能源管理系统是为了降低可再生能源发电的高渗透率所带来的影响，通过管理和调度需求侧的住宅电力和能源消费。了解电能是如何消耗的是该系统的关键步骤。非侵入式负载监测被认为是解决这一问题的最有潜力的方法，其目的是通过分解总功耗来分离家庭中的各个电器。近年来，NILM被定义为一个多标签分类问题，在这一领域有很多研究。在本文中，提出了一种非侵入式的方法，可以识别家电用电信息的总功耗，并进行了深入的研究。首先，通过引入随机森林算法作为基分类器，对随机k-标签集多标签分类算法进行了改进。在此基础上，结合网格搜索法和交叉验证法确定最优参数集。该算法用于实现家电产品的识别。最后，基于辨识结果，采用整数线性规划方法对每个电器，特别是多状态电器的功率进行估计。在低压电网仿真器上的实验结果表明，与传统的随机k-labelset多标签分类方法和其他基分类器相比，该方法具有较高的识别精度，能够准确识别不同电器的用电量。功率估计的理想性能拓宽了基于机器学习的非侵入式能量监测的应用。
Home energy management system is proposed to reduce the influences caused by the high ratio penetration of renewable energy generation, through managing and dispatching the residential power and energy consumption in the demand side. Being aware of how the electric energy is consumed is a key step of this system. Non-intrusive Load Monitoring is regarded as the most potential method to address this problem, which aims to separate individual appliances in households by decomposing the total power consumption. In recent years, NILM is framed as a multi-label classification problem and many researches has been investigated in this field. In this paper, a non-intrusive method which can identify appliances power usage information from the total power consumption is proposed and thoroughly investigated. Firstly, the random k-labelset multi-label classification algorithm is enhanced by introducing random forest algorithm as base classifier. Then, grid search method and cross validation method are integrated to determine the optimal paraments set. This algorithm is used to achieve the appliances identification. Finally, based on the identification result, the integer linear programming is employed for power estimation of each appliance, especially multi-state appliances. Experimental results on low voltage networks simulator demonstrate that the proposed method has a high identification accuracy compared with the traditional random k-labelset multi-label classification methods with other base classifiers, and it is capable of identifying the power usages of different appliances accurately. The desirable performance of power estimation has broadened the applications of machine learning based non-intrusive energy monitoring.
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DOI：
10.1109/icebe.2011.48
发表时间：
2011-10
期刊：
2011 IEEE 8th International Conference on e-Business Engineering
影响因子：
--
作者：
Hsueh-Hsien Chang;Po-Ching Chien;Lung-Shu Lin;N. Chen
通讯作者：
Hsueh-Hsien Chang;Po-Ching Chien;Lung-Shu Lin;N. Chen
影响因子：
4.3
作者：
Fernando Marcos Wittmann;J. López;M. J. Rider
通讯作者：
Fernando Marcos Wittmann;J. López;M. J. Rider
影响因子：
11.7
作者：
Buddhahai, Bundit;Wongseree, Waranyu;Rakkwamsuk, Pattana
通讯作者：
Rakkwamsuk, Pattana
影响因子：
9.6
作者：
R. Torquato;Qingxin Shi;Wilsun Xu;W. Freitas
通讯作者：
R. Torquato;Qingxin Shi;Wilsun Xu;W. Freitas
影响因子：
2.6
作者：
Jiang Lin;Xianfeng Ding;Dan Qu;Hongyan Li
通讯作者：
Jiang Lin;Xianfeng Ding;Dan Qu;Hongyan Li