Authors
Dongyang Zhang1,2, Xiaohu Zhang1 ,Lei Hua1 ,Jian Di1 , Wenqing Zhao1,3 , Yumei Ma1
Affiliations
- Department of Computer Science North China Electric Power University (Baoding);
- Hebei Key Laboratory of Knowledge Computing for Energy & Power;
- Engineering Research Center for Intelligent Computing of Complex Energy Systems, Ministry of Education corresponding author(s): Yumei Ma (yumeim@ncepu.edu.cn)
Abstract: With the development of Non-Intrusive Load Monitoring (NILM), it has become feasible to perform device identification, energy consumption decomposition, and load switching detection using Deep Learning (DL) methods. Similar to other machine learning problems, the research and validation of NILM necessitate substantial data support. Moreover, different regions exhibit distinct characteristics in their electricity environments. Therefore, there is a need to provide open datasets tailored to different regions. In this paper, we introduce the Transient Dataset of Household Appliances with Intensive Switching Events (TDHA1 ). This dataset comprises switch instantaneous data from 10 typical household appliances in China. The TDHA dataset features a high sampling rate, accurate labeling, and realistic representation of actual appliance start-up waveforms. Additionally, appliance switching is achieved through precise control of relay switches, thus mitigating interference caused by mechanical switches. By furnishing such a dataset, we aim not only to enhance the recognition accuracy of existing NILM algorithms but also to facilitate the application of NILM algorithms in regions sharing similar electricity consumption characteristics to those of China.
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