Intelligent Energy Management with IoT Framework in Smart Cities Using Intelligent Analysis: An Application of Machine Learning Methods for Complex Networks and Systems
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866910725904007168 |
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| author | Nikpour, Maryam Yousefi, Parisa Behvand Jafarzadeh, Hadi Danesh, Kasra Shomali, Roya Asadi, Saeed Lonbar, Ahmad Gholizadeh Ahmadi, Mohsen |
| author_facet | Nikpour, Maryam Yousefi, Parisa Behvand Jafarzadeh, Hadi Danesh, Kasra Shomali, Roya Asadi, Saeed Lonbar, Ahmad Gholizadeh Ahmadi, Mohsen |
| contents | This study confronts the growing challenges of energy consumption and the depletion of energy resources, particularly in the context of smart buildings. As the demand for energy increases alongside the necessity for efficient building maintenance, it becomes imperative to explore innovative energy management solutions. We present a comprehensive review of Internet of Things (IoT)-based frameworks aimed at smart city energy management, highlighting the pivotal role of IoT devices in addressing these issues due to their compactness, sensing, measurement, and computing capabilities. Our review methodology encompasses a thorough analysis of existing literature on IoT architectures and frameworks for intelligent energy management applications. We focus on systems that not only collect and store data but also support intelligent analysis for monitoring, controlling, and enhancing system efficiency. Additionally, we examine the potential for these frameworks to serve as platforms for the development of third-party applications, thereby extending their utility and adaptability. The findings from our review indicate that IoT-based frameworks offer significant potential to reduce energy consumption and environmental impact in smart buildings. Through the adoption of intelligent mechanisms and solutions, these frameworks facilitate effective energy management, leading to improved system efficiency and sustainability. Considering these findings, we recommend further exploration and adoption of IoT-based wireless sensing systems in smart buildings as a strategic approach to energy management. Our review underscores the importance of incorporating intelligent analysis and enabling the development of third-party applications within the IoT framework to efficiently meet the evolving energy demands and maintenance challenges |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_05567 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Intelligent Energy Management with IoT Framework in Smart Cities Using Intelligent Analysis: An Application of Machine Learning Methods for Complex Networks and Systems Nikpour, Maryam Yousefi, Parisa Behvand Jafarzadeh, Hadi Danesh, Kasra Shomali, Roya Asadi, Saeed Lonbar, Ahmad Gholizadeh Ahmadi, Mohsen Machine Learning Computers and Society Systems and Control This study confronts the growing challenges of energy consumption and the depletion of energy resources, particularly in the context of smart buildings. As the demand for energy increases alongside the necessity for efficient building maintenance, it becomes imperative to explore innovative energy management solutions. We present a comprehensive review of Internet of Things (IoT)-based frameworks aimed at smart city energy management, highlighting the pivotal role of IoT devices in addressing these issues due to their compactness, sensing, measurement, and computing capabilities. Our review methodology encompasses a thorough analysis of existing literature on IoT architectures and frameworks for intelligent energy management applications. We focus on systems that not only collect and store data but also support intelligent analysis for monitoring, controlling, and enhancing system efficiency. Additionally, we examine the potential for these frameworks to serve as platforms for the development of third-party applications, thereby extending their utility and adaptability. The findings from our review indicate that IoT-based frameworks offer significant potential to reduce energy consumption and environmental impact in smart buildings. Through the adoption of intelligent mechanisms and solutions, these frameworks facilitate effective energy management, leading to improved system efficiency and sustainability. Considering these findings, we recommend further exploration and adoption of IoT-based wireless sensing systems in smart buildings as a strategic approach to energy management. Our review underscores the importance of incorporating intelligent analysis and enabling the development of third-party applications within the IoT framework to efficiently meet the evolving energy demands and maintenance challenges |
| title | Intelligent Energy Management with IoT Framework in Smart Cities Using Intelligent Analysis: An Application of Machine Learning Methods for Complex Networks and Systems |
| topic | Machine Learning Computers and Society Systems and Control |
| url | https://arxiv.org/abs/2306.05567 |