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Article . 2026
License: CC BY
Data sources: Datacite
ZENODO
Article . 2026
License: CC BY
Data sources: Datacite
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Smart Energy Management System Using Ai and Iot

Authors: P. Logeswari; G. V. Sowmiya; D. Keerthana;

Smart Energy Management System Using Ai and Iot

Abstract

The rapid growth of population, industrialization, and urbanization has led to a continuous increase in global energy demand, creating serious challenges related to energy availability, cost, efficiency, and environmental sustainability. Traditional energy management systems are often inefficient, manual, and reactive in nature, leading to energy wastage, poor resource utilization, and high operational costs. In this context, the integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies offers a powerful and intelligent solution for building Smart Energy Management Systems (SEMS). These advanced systems enable real-time monitoring, automated control, intelligent decision-making, and optimized energy usage across residential, commercial, and industrial environments. IoT devices continuously monitor parameters such as electricity consumption, voltage levels, equipment performance, environmental conditions, and user behavior. This data is transmitted to centralized systems or cloud platforms, where it is stored and processed for further analysis. Based on these insights, AI systems can make intelligent decisions such as load balancing, peak demand management, energy optimization, and automated energy distribution. A Smart Energy Management System using AI and IoT enables automated control of electrical appliances, lighting systems, heating and cooling systems, and industrial machinery. These systems can automatically switch devices on or off, regulate power supply, and optimize energy usage based on real-time demand and user preferences

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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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