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Publication Type : Journal Article
Publisher : Cognitive Systems Research (ELSEVIER), vol. 57, pp. 131-138, 2019.
Source : Cognitive Systems Research (ELSEVIER), vol. 57, pp. 131-138, 2019
Keywords : Demand responseSmart gridHome energy managementHTOU pricingSchedulable applianceSupervised learning algorithm
Campus : Chennai
School : School of Engineering
Center : Amrita Innovation & Research
Department : Electronics and Communication
Verified : Yes
Year : 2019
Abstract : Demand Response (DR) is a key attribute to enhance the operation of smart grid. Demand response improves the performance of the electric power systems and also deals with peak demand issues. Demand Response (DR) implementation for residential consumers is potentially accredited by Home Energy Management System (HEMS). This paper presents an algorithm for home energy management system to shift the schedulable loads in a residential home, that neglects consumer discomfort and minimizes electricity bill of energy consumption using Hourly-Time-Of-Use (HTOU) pricing scheme. Supervised learning algorithm is used in this paper to learn the usage patterns of consumers to allow schedulable appliances at a residential home to autonomously overcome consumer discomfort. Simulation results confirms that the proposed algorithm effectively decreases consumer electricity bill, decreases peak load demand and also avoids consumer discomfort.
Cite this Research Publication : Ganesh Kumar Chellamani and Premanand Venkatesh Chandramani, “Demand response management system with discrete time window using supervised learning algorithm”, Cognitive Systems Research (ELSEVIER), vol. 57, pp. 131-138, 2019.