DING Xun, ZHANG Zhong, XIA Zhaojun, FAN Yangyang, ZHANG Ying, KONG Liang. Research on the Home Intelligent Energy Management System Based on Noninvasive Load Monitoring[J]. Modern Electric Power, 2022, 39(4): 496-504. DOI: 10.19725/j.cnki.1007-2322.2021.0140
Citation: DING Xun, ZHANG Zhong, XIA Zhaojun, FAN Yangyang, ZHANG Ying, KONG Liang. Research on the Home Intelligent Energy Management System Based on Noninvasive Load Monitoring[J]. Modern Electric Power, 2022, 39(4): 496-504. DOI: 10.19725/j.cnki.1007-2322.2021.0140

Research on the Home Intelligent Energy Management System Based on Noninvasive Load Monitoring

  • With the integration of multi-energy networks and the rapid development of energy Internet technology, household energy management plays an important role in solving the problem of supply and demand of each energy network node. Most of the existing household energy consumption management is optimized for the known power load, while the diversification of the types of electrical equipments and the sudden increase of electrical equipments are not considered. On the basis of the noninvasive load monitoring (abbr. NILM) algorithm, the household load electricity consumption law and information that provide data support for household smart energy management can be effectively obtained. A multi-objective optimization model of smart home energy consumption, in which the household electricity cost, temperature, time and comfort level were taken as objective functions, was established, and the controllable load, EV and energy storage system were analyzed and corresponding mathematical models were proposed and solved by particle swarm algorithm. Simulation results show that based on NILM algorithm and only considering electricity cost and comfort level, the home power utilization cost can reduced by 72.5%. When user-controllable power load increases, the control strategy can be updated by NILM in realtime to decrease user’s electricity utilization cost. Results of multiple calculations for different users show that the net cost and computing time fluctuate slightly, thus the rationality and reliability of the NILM algorithm can meet the requirements of family intelligent energy consumption.
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