Autonomic IoT: Towards Smart System Components with Cognitive IoT

Agyemang, Justice Owusu; Yu, D.; Kponyo, Jerry John

doi:10.1007/978-3-030-93314-2_16

Cited by 8 publications

(2 citation statements)

References 21 publications

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“…Deep learning provides better performance over classical machine learning. DL allows achieving unprecedented levels of sensory and analytic intelligence using the most cost-effective and energy-efficient embedded processors [66].…”

Section: Deep Learning Algorithmsmentioning

confidence: 99%

The Role of Machine Learning and the Internet of Things in Smart Buildings for Energy Efficiency

et al. 2022

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Machine learning can be used to automate a wide range of tasks. Smart buildings, which use the Internet of Things (IoT) to connect building operations, enable activities, such as monitoring temperature, safety, and maintenance, for easier controlling via mobile devices and computers. Smart buildings are becoming core aspects in larger system integrations as the IoT is becoming increasingly widespread. The IoT plays an important role in smart buildings and provides facilities that improve human security by using effective technology-based life-saving strategies. This review highlights the role of IoT devices in smart buildings. The IoT devices platform and its components are highlighted in this review. Furthermore, this review provides security challenges regarding IoT and smart buildings. The main factors pertaining to smart buildings are described and the different methods of machine learning in combination with IoT technologies are also described to improve the effectiveness of smart buildings to make them energy efficient.

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Section: Deep Learning Algorithmsmentioning

confidence: 99%

The Role of Machine Learning and the Internet of Things in Smart Buildings for Energy Efficiency

et al. 2022

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“…The escalating consumption of electrical equipment in recent years has heightened environmental concerns, prompting the scientific community to prioritize alternative energy sources [1]. Addressing the challenge of ever-increasing electricity demand is complex.…”

Section: Introductionmentioning

confidence: 99%

Design and deployment of a novel Decisive Algorithm to enable real-time optimal load scheduling within an Intelligent Smart Energy Management System based on IoT

Rao,

Sahoo,

Yanine

2024

Preprint

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Consumers routinely use electrical devices, leading to a disparity between consumer demand and the supply side a significant concern for the energy sector. Implementing demand-side energy management can enhance energy efficiency and mitigate substantial supply-side shortages. Current energy management practices focus on reducing power consumption during peak hours, enabling a decrease in overall electricity costs without sacrificing usage. To tackle the mentioned challenges and maintain system equilibrium, it is essential to develop a flexible and portable system. Introducing an intelligent energy management system could pre-empt power outages by implementing controlled partial load shedding based on consumer preferences. During a demand response event, the system adapts by imposing a maximum demand limit, considering various scenarios and adjusting appliance priorities. Experimental work, incorporating user comfort levels, sensor data, and usage times, is conducted using Smart Energy Management Systems (SEMS) integrated with cost-optimization algorithms.

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Intelligent power management system for optimizing load strategies in renewable generation

Rao,

Sahoo,

Yanine

2024

Electr Eng

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Autonomic IoT: Towards Smart System Components with Cognitive IoT

Cited by 8 publications

References 21 publications

The Role of Machine Learning and the Internet of Things in Smart Buildings for Energy Efficiency

The Role of Machine Learning and the Internet of Things in Smart Buildings for Energy Efficiency

Design and deployment of a novel Decisive Algorithm to enable real-time optimal load scheduling within an Intelligent Smart Energy Management System based on IoT

Intelligent power management system for optimizing load strategies in renewable generation

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