Non-intrusive load monitoring algorithm based on household electricity use habits

被引：0
作者：
Bo Yin
Zhenhuan Li
Jiali Xu
Lin Li
Xinghai Yang
Zehua Du
机构：
[1] Ocean University of China,College of Information Science and Engineering
[2] Pilot National Laboratory for Marine Science and Technology,School of Information Science and Technology
[3] Qingdao University of Science and Technology,undefined
来源：
Neural Computing and Applications | 2022年 / 34卷
关键词：
Factorial hidden Markov model; Gaussian mixture model; Non-intrusive load monitoring; Household electricity habits;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
The construction of smart grid is an important part of improving the utilization rate of electric energy. As an important way for the construction of smart grid, non-intrusive load decomposition methods have been extensively studied. In this type of method, limited by transmission cost and network bandwidth, low-frequency data has been widely used in practical applications. However, the accuracy of device identification in this case faces challenges. Due to the relatively single characteristics of low-frequency data, it is difficult to express the operating status of complex electrical appliances, resulting in low decomposition performance. In this paper, a non-intrusive load is proposed based on household electrical habits by studying the relationship between household electricity consumption habits and load status decomposition method. The Gaussian mixture model and time information are used to model the probability distribution of the electrical appliance state. This probability distribution is then used as the observation probability distribution of the factor hidden Markov model. In such a way, the BH-FHMM model is proposed. Finally, load decomposition is carried out through the load decomposition process of the FHMM model. In order to verify the performance of the proposed method, an experimental comparison is conducted based on the REDD data set. According to the results, a significant improvement in equipment recognition accuracy is obtained.
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收藏
页码：15273 / 15291
页数：18
相关论文
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