Improved LSTM data analysis system for IoT-based smart classroom

Issue title: Special Section: Intelligent Algorithms for Complex Information Services - Recent Advances and Future Trends

Guest editors: Andino Maseleno, Xiaohui Yuan and Valentina E. Balas

Article type: Research Article

Authors: Li, Yingjie^a | Chen, Lianjun^{b; *}

Affiliations: [a] Information School, Shanghai Ocean University, Shanghai, China | [b] Information Technology School, Shanghai Jianqiao University, Shanghai, China

Correspondence: [*] Corresponding author. Lianjun Chen, Information Technology School, Shanghai Jianqiao University, Shanghai, 201306, China. E-mail: 01022@gench.edu.cn.

Abstract: To reduce the resource and energy waste of colleges and universities more accurately and efficiently, this paper has developed a smart classroom data analysis system based on the Internet of Things, which realizes a variety of sensor information (temperature, humidity, smoke). Environmental parameters such as carbon dioxide concentration and light intensity), remote collection of equipment information, data storage and data analysis functions, and intelligent control of smart classrooms. Data analysis uses an improved LSTM model to predict energy consumption. The model uses LSTM and bidirectional LSTM and uses the ELU activation function instead of the sigmoid and tanh activation functions of the LSTM. Compared with the standard LSTM model and the LSTM model without the ELU activation function, the model improves the prediction accuracy, better avoid the gradient disappearance, and reduces the over-fitting. The system implementation results show that the system can effectively reduce school energy waste.

Keywords: Internet of things, smart classroom, data analysis, Bi-LSTM

DOI: 10.3233/JIFS-179999

Journal: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 4, pp. 5141-5148, 2020