Smart City Gnosys

Smart city article details

Title	Iot Based Energy Monitoring And Outage Reporting
ID_Doc	33658
Authors	Boobalan S.; Celva Choudri M.; Haari V.S.; Gunasekar N.; Gowtham V.
Year	2021
Published	2021 7th International Conference on Advanced Computing and Communication Systems, ICACCS 2021
DOI	http://dx.doi.org/10.1109/ICACCS51430.2021.9441894
Abstract	Leading to a significant rise demand in electrical energy from the quickly rising population of the world, will cause a dearth of electrical energy in the future world. Many smart devices will be incorporated into residentials in smart cities that participate in the electricity bazaar through the response of demand programs with the creation of the IoT to effectively respond to demand (DR) programs. Manage energy to meet this growing demand for energy. With this opportunity, an energy management plan for IoT-enabled residential buildings is therefore built using a price-based DR program. We propose an algorithm for WBFA (wind-driven bacterial foraging), which is a combination of algorithms for WDO (Wind driven optimization) and BFO (Bacterial Foraging Optimization). With algorithms, Continuously, we built a plan based on our proposed WBFA to control the power consumption of smart IoT-enabled residentials appliance by planning to reduce the PAR (Peak-to-Average Ratio), reduce energy costs and optimize user comfort (UC). This increases the productive use of electricity, which in turn increases IoT-enabled residential sustainability. In order to combat the important problems of DR programs, the WBFA-based strategy responds to price-based DR programs, which is the weakness of the knowledge of customers to respond while receiving DR signals. Substantial simulations are being carried out to help the productivity and efficiency of the proposed idea of the WBFA-based strategy. In addition, the proposed WBFA-based approach is compared with benchmark strategies, including the algorithm for BPSO (Binary Particle Swarm Optimization), GA (Genetic Algorithm), the algorithm for GWDO (Genetic Wind-Driven Optimization) and the algorithm for genetic binary particle swarm optimization (GBPSO) in terms of consumption of energy, electricity cost, UC and PAR. © 2021 IEEE.
Author Keywords	Energy Monitoring; IoT; Peak-to-Average Ratio; Power Demand Management; Wireless

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