Transient dataset of household appliances with Intensive switching events

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 nece...

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Bibliographic Details
Published in:Scientific data 2024-05, Vol.11 (1), p.493-493, Article 493
Main Authors: Zhang, Dongyang, Zhang, Xiaohu, Hua, Lei, Di, Jian, Zhao, Wenqing, Ma, Yumei
Format: Article
Language:English
Subjects:
639/4077/2790
706/4066/4075
Algorithms
Appliances
Data Descriptor
Datasets
Deep learning
Electricity
Energy consumption
Humanities and Social Sciences
Labeling
multidisciplinary
Science
Science (multidisciplinary)
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Summary: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 (TDHA 25 ). This dataset comprises switch instantaneous data from 10 typical household appliances in China. The TDHA dataset features a high sampling rate, accurate labelling, 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.
ISSN:2052-4463
2052-4463
DOI:10.1038/s41597-024-03310-3