电信科学 ›› 2023, Vol. 39 ›› Issue (1): 60-71.doi: 10.11959/j.issn.1000-0801.2023017

• 研究与开发 • 上一篇    下一篇

数字孪生辅助的智能楼宇多模态通信资源管理方法

石珵1,2, 刘朋矩1, 杜治钢1, 张孙烜1, 周振宇1, 白晖峰3, 何国庆4, 孙文文4, 马跃5   

  1. 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
  • 修回日期:2023-01-09 出版日期:2023-01-20 发布日期:2023-01-01
  • 作者简介:石珵(1984- ),女,华北电力大学新能源电力系统国家重点实验室博士生,广州城市理工学院讲师,主要研究方向为零碳建筑、智能建筑、建筑通信、智能建筑设计理论等
    刘朋矩(1997- ),男,华北电力大学新能源电力系统国家重点实验室硕士生,主要研究方向为电力物联网资源分配与网络安全
    杜治钢(1999- ),男,华北电力大学新能源电力系统国家重点实验室硕士生,主要研究方向为电力物联网
    张孙烜(1998- ),男,华北电力大学新能源电力系统国家重点实验室博士生,主要研究方向为电力物联网资源分配与网络安全
    周振宇(1983- ),男,华北电力大学新能源电力系统国家重点实验室教授,主要研究方向为无线通信网络与新技术、物联网与现代传感技术、能源互联网信息通信技术等
    白晖峰(1984- ),男,博士,北京智芯微电子科技有限公司高级工程师,主要研究方向为信息通信、光学互联网等
    何国庆(1981- ),男,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教授级高级工程师,主要研究方向为新能源并网稳定性分析与控制
    孙文文(1990- ),男,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工程师,主要研究方向为可再生能源发电及其并网技术
    马跃(1977- ),男,国网冀北电力有限公司高级工程师,主要研究方向为电力通信
  • 基金资助:
    国家电网有限公司总部管理科技项目(52094021N010(5400-202199534A-0-5-ZN))

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

Cheng SHI1,2, Pengju LIU1, Zhigang DU1, Sunxuan ZHANG1, Zhenyu ZHOU1, Huifeng BAI3, Guoqing HE4, Wenwen SUN4, Yue MA5   

  1. 1 State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China
    2 Guangzhou City University of Technology, Guangzhou 510800, China
    3 Beijing Smartchip Microelectronics Technology Co., Ltd., Beijing 100192, China
    4 State Key Laboratory of Operation and Control of Renewable Energy and Storage, China Electric Power Research Institute, Beijing 100192, China
    5 State Grid Jibei Electric Power Co., Ltd., Beijing 100054, China
  • Revised:2023-01-09 Online:2023-01-20 Published:2023-01-01
  • Supported by:
    The Science and Technology Project of State Grid Corporation of China(52094021N010(5400-202199534A-0-5-ZN))

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。

关键词: 智能楼宇, 数字孪生, 能源调控, 联邦学习, 匹配理论, 上置信区间

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.

Key words: smart building, digital twin, energy regulation, federated learning, matching theory, upper confidence bound

中图分类号: 

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