Objective: The application of genetic algorithm in the design of the Internet of Things platform of heat energy collection system is launched, and the value of the application of the Internet of Things control platform of genetic algorithm in the heat energy collection system is analyzed to verify its effectiveness and superiority. Method: Firstly, the design of the basic framework of the Internet of Things is developed, mainly on the acquisition and processing module and the communication module of the framework. Then, genetic algorithm is used to make statistics of the system nodes in the heat energy collection system, and establish the optimization function of heat energy conversion rate based on genetic algorithm. Then, the optimization function is used to design the control circuit to improve the flexibility of the control circuit. Finally, the system developed in this study is verified by experiments. Results: After the application of three platforms to control the heat energy collection system, the heat energy conversion rate is higher than that of the previous platform, and the platform controlled thermal energy conversion rate is the highest. Moreover, the real-time energy consumption is the smallest. Conclusion: It is found that the application of genetic algorithm in the Internet of Things platform of the heat energy collection system can effectively optimize the heat energy conversion rate, and the real-time work energy consumption is also very low, which improves the heat energy standardization rate, has certain application value, and can better improve the energy source rate.
PAPER SUBMITTED: 2019-11-13
PAPER REVISED: 2019-12-22
PAPER ACCEPTED: 2020-01-13
PUBLISHED ONLINE: 2020-03-15
, VOLUME 24
, ISSUE Issue 5
, PAGES [3177 - 3184]
- Edoardo, P., et al., Genetic-algorithm Based Method for Mitigating Label Noise Issue in ECG Signal Classification. Biomedical Signal Processing and Control, 19 (2015), pp. 130-136.
- Jayanthi, S., et al., Internet of Things (IOT) Based Generous Transformational Optimization Algorithm (GTOA) for Hybrid Renewable Energy System Synchronization and Status Monitioring. Wireless Personal Communications, 102 (2018), 4, pp. 2597-2618.
- Shaofei Wu. Study and evaluation of clustering algorithm for solubility and thermodynamic data of glycerol derivatives, Thermal Science, 23(2019), 5, pp.2867-2875
- Li, J., et al., Magnetotelluric noise suppression based on matching pursuit and genetic algorithm. Chinese Journal of Geophysics- Chinese Edition, 61 (2018), 7, pp. 3086-3101.
- Enji Sun. Internet of Things Based Combustible Ice Safety Monitoring System Framework
- Vigneswaran, V.S., et al., Performance evaluation of solar box cooker assisted with latent heat energy storage system for cooking application. Iop Conference, 67 (2017), 1, pp. 012017.
- Qie S., et al., A Comprehensive Review of Smart Energy Meters in Intelligent Energy Networks. IEEE Internet of Things Journal, 3 (2015), 4, pp. 464-479.
- Xiaolong X., et al., CLOTHO: A Large-Scale Internet of Things based Crowd Evacuation Planning System for Disaster Management. IEEE Internet of Things Journal, 99 (2018), pp. 1-1.
- Mianxiong D., et al., RMER: Reliable and Energy-Efficient Data Collection for Large-Scale Wireless Sensor Networks. IEEE Internet of Things Journal, 3 (2017), 4, pp. 511-519.
- Shaofei Wu, Mingqing Wang, Yuntao Zou. Bidirectional cognitive computing method supported by cloud technology, Cognitive Systems Research, 52(2018), pp. 615-621
- Yuxin L., et al., FFSC: An Energy Efficiency Communications Approach for Delay Minimizing in Internet of Things. IEEE Access, 4 (2017), pp. 3775-3793.
- Saleem A., et al., Energy and Spectral Efficient Cognitive Radio Sensor Networks for Internet of Things. IEEE Internet of Things Journal, 99 (2018), pp. 1-1.
- Chang Z.S., et al., A Framework for Incorporating Demand Response of Smart Buildings into the Integrated Heat and Electricity Energy System. IEEE Transactions on Industrial Electronics, 99 (2017), pp. 1-1.
- Xiong L., et al., A Robust and Energy Efficient Authentication Protocol for Industrial Internet of Things. IEEE Internet of Things Journal, 99 (2017), pp. 1-1.