Temperature monitoring and prediction under different transmission modes. (June 2021)

• Record Type: Journal Article
• Authors: Chen, Wanpei; Yang, Qinrong; Gao, Shen; Zhang, Tao; Han, Heng
• Is Part Of: Computers & electrical engineering. Volume 92(2021)
• Publication Date: 2021-06
• Subjects: Zigbee -- LoRA -- LSTM-ELM network -- Reliability; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107140
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: With the increasing maturity of Internet of Things technology, temperature sensors based on Zigbee, LoRa and other transmission modes have successively appeared. However, due to factors such as outdoor environment and transmission distance, the temperature measurement data under different transmission methods may be inaccurate and the update of background data may be delayed. To master the temperature measurement system parameters in different transmission modes in detail, Zigbee and LoRa transmission modes are used to design temperature sensors respectively. At the same time, the prediction accuracy and running time of the line temperature under several existing neural networks are analyzed and compared, then based on the LSTM network with the highest accuracy, a temperature prediction method based on Long Short Time Memory and Extreme Learning Machine(LSTM-ELM) network is proposed to realize the rapid prediction of line temperature. Simulation results show that the network prediction accuracy based on LSTM-ELM reaches 93.32%, and the prediction time is greatly reduced to 875.75 s, which is nearly 2000% higher than the traditional LSTM network, and can provide a reliable basis for background management in engineering practice.