Digital twin-assisted multi-mode communication resource management methods for smart buildings

Cheng SHI; Pengju LIU; Zhigang DU; Sunxuan ZHANG; Zhenyu ZHOU; Huifeng BAI; Guoqing HE; Wenwen SUN; Yue MA

doi:10.11959/j.issn.1000-0801.2023017

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Digital twin-assisted multi-mode communication resource management methods for smart buildings

Research and Development | 更新时间：2024-06-05

- Digital twin-assisted multi-mode communication resource management methods for smart buildings
- Telecommunications Science Vol. 39, Issue 1, Pages: 60-71(2023)
- 作者机构：
  
  1. 华北电力大学新能源电力系统国家重点实验室，北京 102206
  2. 广州城市理工学院，广东广州 510800
  3. 北京智芯微电子科技有限公司，北京 100192
  4. 中国电力科学研究院有限公司新能源与储能运行控制国家重点实验室，北京 100192
  5. 国网冀北电力有限公司，北京 100054
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2023017
  CLC： TN914
- Published Online：2023-01，
  
  Published：20 January 2023
- 稿件说明：
移动端阅览
Cheng SHI, Pengju LIU, Zhigang DU, et al. Digital twin-assisted multi-mode communication resource management methods for smart buildings[J]. Telecommunications science, 2023, 39(1): 60-71.
DOI：

Cheng SHI, Pengju LIU, Zhigang DU, et al. Digital twin-assisted multi-mode communication resource management methods for smart buildings[J]. Telecommunications science, 2023, 39(1): 60-71. DOI： 10.11959/j.issn.1000-0801.2023017.

摘要

多模态通信网络为智能楼宇能源调控数据的采集、传输、处理以及能源调控模型训练提供了通信支撑。数字孪生可以提供计算资源、信道特性等状态估计，辅助多模态通信资源管理优化，提高能源调控模型训练精度。然而，数字孪生辅助的智能楼宇多模态通信资源管理面临能源调控模型训练误差大、多时间尺度资源分配耦合、模型训练精度提高与能耗优化相互矛盾等挑战。针对上述挑战，提出基于数字孪生和经验匹配学习的多时间尺度通信资源管理优化算法，通过联合优化大时间尺度网关选择和小时间尺度信道分配与功率控制，最小化全局模型损失函数和能耗加权和。仿真结果表明，所提算法可以提高全局模型损失函数和能耗加权和性能，保障智能楼宇能源精准调控需求，促进智能楼宇能源调控低碳运行。

Abstract

The multi-mode communication network provides communication support for the collection

transmission

and processing of energy regulation data and the training of energy regulation models for smart buildings.Digital twin can provide state estimation of computing resources and channel characteristics

assist in the multi-mode communication resource optimization management

and improve the training precision of energy regulation models.However

the digital twin-assisted multi-mode communication resource management of smart buildings still face challenges such as large training error of energy regulation model

coupling of multi-timescale resource allocation

and contradictions between training precision improvement of energy regulation model and energy consumption optimization.Aiming at the above challenges

a multi-timescale communication resource management optimization algorithm based on digital twin and empirical matching learning was proposed.The weighted sum of global model loss function and energy consumption was minimized by jointly optimizing the large-timescale gateway selection and small-timescale channel allocation and power control.Simulation results show that the proposed algorithm can improve the performance of weighted sum of global model loss function and energy consumption

ensure the precise energy regulation requirement and promote the low-carbon operation of smart buildings.

关键词

Keywords

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