Energy management system design for high energy consuming enterprises integrating the Internet of Things and neural networks

Zhaolin Wang; Zhiping Zhang

doi:10.4108/ew.4849

Authors

Zhaolin Wang Chongqing College of Finance and Economics
Zhiping Zhang Chongqing College of Finance and Economics

DOI:

https://doi.org/10.4108/ew.4849

Keywords:

Internet of Things technology, Neural Networks, Energy Management, High energy consuming enterprises, Prediction and optimization control

Abstract

INTRODUCTION: High energy consuming enterprises continue to pay increasing attention to energy consumption. Therefore, designing an energy management system is significant.

OBJECTIVES: To improve the management level and economic benefits of enterprises, a high energy consuming enterprise energy management system design based on Internet of Things technology and neural network algorithms is proposed.

METHODS: Internet of Things devices are used for data collection and transmission. The combination of neural network model prediction and optimization algorithms can achieve real-time monitoring, prediction, and optimization control of energy consumption.

RESULTS: The research results indicated that the response time of the high energy consuming enterprise energy management system proposed in the study was 80.2 ms when the number of people was 600. The fluctuation range of CPU usage within 24 hours was 14% to 45%.

CONCLUSION: A high energy consuming enterprise energy management system that integrates the Internet of Things and neural networks can manage energy more efficiently and intelligently, thereby improving the production efficiency and economic benefits of the enterprise. This helps companies gain greater advantages in fierce market competition.

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