Intelligent Edge Computing for IoT-Based Energy Management in Smart Cities
Abstract
In recent years, green energy management systems (smart grid, smart buildings, and so on) have received huge research and industrial attention with the explosive development of smart cities. By introducing Internet of Things (IoT) technology, smart cities are able to achieve exquisite energy management by ubiquitous monitoring and reliable communications. However, long-term energy efficiency has become an important issue when using an IoT-based network structure. In this article, we focus on designing an IoT-based energy management system based on edge computing infrastructure with deep reinforcement learning. First, an overview of IoT-based energy management in smart cities is described. Then the framework and software model of an IoT-based system with edge computing are proposed. After that, we present an efficient energy scheduling scheme with deep reinforcement learning for the proposed framework. Finally, we illustrate the effectiveness of the proposed scheme.
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Edge computing and deep RL can efficiently schedule energy use at building, microgrid, and grid levels.
“Edge computing can be used to process data from distributed sensors and other Internet of Things devices, and deep RL can then use this data to efficiently schedule energy use [489], at the level of single or multiple buildings, or at the microgrid and grid level [631].”
From Tackling climate change with machine learning (2023) · cites this paper as [489]
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References 17 · Cited by 595
- Authors
- Liu, Y., Yang, C., Jiang, L., Xie, S., & Zhang, Y.
- Affiliations
- Guangdong University of Technology (CN)
- University of Oslo (NO)
- Venue
- IEEE Network
- Published
- 2019-03
- Volume
- 33
- Issue
- 2
- Pages
- 111-117
- ISSN
- 0890-8044 (ISSN-L)
- 1558-156X
- OpenAlex
- W2926519916
- Cited by
- 595
- References
- 17
- Citations by year
- 2026: 35 · 2025: 64 · 2024: 83 · 2023: 92 · 2022: 101 · 2021: 114 · 2020: 73 · 2019: 33
- FWCI
- 43.90
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Network (publishedVersion)
- Funders
- National Natural Science Foundation of China
- Beijing University of Posts and Telecommunications
- South China University of Technology
- Central China Normal University
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Keywords
- edge computing, Internet of Things, energy management, smart cities, deep reinforcement learning, energy scheduling, smart grid, smart buildings, energy efficiency
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