Dynamic appliance scheduling and energy management in smart homes using adaptive reinforcement learning techniques

Smart home energy management is complicated because of varying user preferences, expenses, and consumption. These dynamics are difficult for traditional systems to handle, but new developments in reinforcement learning and optimization may be able to help. The paper introduces a novel Demand Respons...

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Bibliographic Details
Published in:	Scientific reports Vol. 15; no. 1; pp. 24594 - 26
Main Authors:	Saroha, Poonam, Singh, Gopal, Lilhore, Umesh Kumar, Simaiya, Sarita, Khan, Monish, Alroobaea, Roobaea, Alsafyani, Majed, Alsufyani, Hamed
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 09.07.2025 Nature Publishing Group Nature Portfolio
Subjects:	639/166 639/4077 639/705 Algorithms Alternative energy sources Appliance scheduling Appliances Cost control Cost reduction Decision making Deep learning Deep Q network Demand response Demand side management Electricity Energy consumption Energy efficiency Energy management Energy prices Energy resources Energy usage Forecasting techniques Humanities and Social Sciences Learning Machine learning Management multidisciplinary Optimization Performance assessment Product reviews Reinforcement Renewable resources Scheduling Science Science (multidisciplinary) Self-adaptive Puma optimizer algorithm Smart houses Trends User satisfaction Deep Q network Appliance scheduling Energy consumption Demand response Self-adaptive Puma optimizer algorithm
ISSN:	2045-2322, 2045-2322
Online Access:	Get full text
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