
Research Article
Research on Energy Consumption Data Monitoring of Smart Parks Based on IoT Technology
@INPROCEEDINGS{10.1007/978-3-031-50546-1_2, author={Hao Zhu}, title={Research on Energy Consumption Data Monitoring of Smart Parks Based on IoT Technology}, proceedings={Advanced Hybrid Information Processing. 7th EAI International Conference, ADHIP 2023, Harbin, China, September 22-24, 2023, Proceedings, Part II}, proceedings_a={ADHIP PART 2}, year={2024}, month={3}, keywords={Internet of Things Technology Smart Park Zigbee Grey Short - and Long-Term Memory Network}, doi={10.1007/978-3-031-50546-1_2} }
- Hao Zhu
Year: 2024
Research on Energy Consumption Data Monitoring of Smart Parks Based on IoT Technology
ADHIP PART 2
Springer
DOI: 10.1007/978-3-031-50546-1_2
Abstract
Intelligent park energy consumption data monitoring is the basis of effective energy management. By monitoring energy consumption data, you can understand the energy consumption of each equipment, system and region in the park, and analyze and evaluate energy use. This helps identify energy consumption problems, optimize energy use, and develop sound energy management practices. Therefore, a smart park energy consumption data monitoring method based on Internet of Things technology is proposed. The perception layer of the Internet of Things technology is used to control the energy of the smart park through the collection, transmission and control of monitoring data. The energy consumption data of each layer of the large-scale smart park is collected through the sensor network. The host computer uses USB interface to obtain data from the gateway. Based on this, the energy consumption data is preprocessed by using power error data correction and missing data fitting compensation steps. By using Gaussian function to analyze the characteristics of energy consumption sample data of the smart park, a multiple linear regression model is constructed to complete the monitoring of energy consumption data of the smart park. The experimental results show that the smart park energy consumption sequence under the proposed method is more stable in fit degree, more accurate in prediction and shorter in response time.