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Reports Outline Computers Study Findings from Zhongnan University of Economics & Law (A Deep Learning Based Non-intrusive Household Load Identification for Smart Grid In China)

  • Oct 14, 2021 12:07 pm GMT
  • 118 views
Source: 
Daily China News

2021 OCT 13 (NewsRx) -- By a News Reporter-Staff News Editor at Daily China News -- Current study results on Computers have been published. According to news reporting originating from Wuhan, People’s Republic of China, by NewsRx correspondents, research stated, “Load identification have shown significant performance gains in Chinese smart grids. Most existing load identification algorithms are based on electrical characteristics of a steady or transient state, which are therefore limited by feature selection and analysing pattern.”

Funders for this research include National Key Research and Development Program of China, National Natural Science Foundation of China (NSFC), key research and development plan of Shaanxi province, Ministry of Education, China, Xi’an Key Laboratory of Mobile Edge Computing and Security, Fundamental Research Funds for the Central Universities, Nanjing University, Xi’an Science and Technology Plan.

Our news editors obtained a quote from the research from the Zhongnan University of Economics & Law, “To address the above issues, this paper proposes the use of the deep neural network for load identification in a Non-Intrusive Load Monitoring (NILM) test-bed, which is set up by introducing diversified household appliances with different load characteristics, to collect the real-time power usage of appliances in a typical Chinese home. The collected load dataset are then sampled, preprocessed and input to the CNN-LSTM framework for training and features extraction. Next, according to several experiments, the structure of our CNN-LSTM network is determined with reasonable hyper-parameters initialised.”

According to the news editors, the research concluded: “Numerical results show that our model is superior to the k-NN, SVM, LSTM and CNN load identification methods, with the average recognition accuracy of 99%, across different kinds of appliances enabled in the typical power grid in China.”

This research has been peer-reviewed.

For more information on this research see: A Deep Learning Based Non-intrusive Household Load Identification for Smart Grid In China. Computer Communications, 2021;177:176-184. Computer Communications can be contacted at: Elsevier, Radarweg 29, 1043 Nx Amsterdam, Netherlands. (Elsevier - www.elsevier.com; Computer Communications - http://www.journals.elsevier.com/computer-communications/)

The news editors report that additional information may be obtained by contacting Shaohua Wan, Zhongnan University of Economics & Law, School of Information & Safety Engineering, Wuhan 430073, People’s Republic of China. Additional authors for this research include Chen Chen, Pinghang Gao, Jiange Jiang, Hao Wang and Pu Li.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.comcom.2021.06.023. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

 

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