Design and prospect of distributed electric heating interactive mode based on federated learning

doi:10.3969/j.issn.2097-0706.2024.01.007

Abstract

Abstract:

With the introduction of the dual-carbon target and the implementation of the " replacing coal with electricity " policy， substantial electric heaters are bound to be connected to the power grid and replace the traditional coal-fired heaters. The electric heater can be used as demand-side adjustable resources for new energy consumption. With regard to their management methods， distributed electric heaters are geographically scattered， while the traditional centralized heaters are vulnerable to privacy leakage and data islands. As a distributed technology， federated learning can support the interaction of electric heating loads under the premise of protecting privacy， and has strong applicability in the field of distributed electric heating interaction. In the analysis on the requirements of distributed electric heating interaction based on federated learning， the applications of edge caching， privacy protection， communication transmission optimization and heterogeneous resource fusion in the interactive modes are expounded. The prospect of distributed electric heating interactive modes based on federated learning is made.

Key words: electric heating interaction, federated learning, edge cache, privacy protection, communication transmission optimization, heterogeneous integration technology

CLC Number:

TK01

LI Bin, BAI Xuefeng, LI Zhichao, WANG Shijun, LIU Chun, CHENG Ziyun. Design and prospect of distributed electric heating interactive mode based on federated learning[J]. Integrated Intelligent Energy, 2024, 46(1): 56-64.

Figures/Tables 6

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

References 55

[1]	别朝红, 林超凡, 李更丰, 等. 能源转型下弹性电力系统的发展与展望[J]. 中国电机工程学报, 2020, 40(9):2735-2744.
	BIE Chaohong, LIN Chaofan, LI Gengfeng, et al. Development and prospect of resilient power system in the context of energy transition[J]. Proceedings of the CSEE, 2020, 40(9): 2735-2744.
[2]	王盛, 谈健, 史文博, 等. 英国新型电力系统建设经验以及对我国省级电网发展启示[J]. 综合智慧能源, 2022, 44(7): 19-32. doi: 10.3969/j.issn.2097-0706.2022.07.003
	WANG Sheng, TAN Jian, SHI Wenbo, et al. Practices of the new power system in the UK and inspiration for the development of provincial power systems in China[J]. Integrated Intelligent Energy, 2022, 44(7): 19-32. doi: 10.3969/j.issn.2097-0706.2022.07.003
[3]	严干贵, 蔡长兴, 段双明, 等. 考虑电池储能单元分组优化的微电网运行控制策略[J]. 电力系统自动化, 2020, 44(23): 38-46.
	YAN Gangui, CAI Changxing, DUAN Shuangming, et al. Operation control strategy of microgrid considering grouping optimization of battery energy storage units[J]. Automation of Electric Power Systems, 2020, 44(23): 38-46.
[4]	刘吉臻, 王玮, 胡阳, 等. 新能源电力系统控制与优化[J]. 控制理论与应用, 2016, 33(12): 1555-1561.
	LIU Jizhen, WANG Wei, HU Yang, et al. Control and optimization of alternate electrical power system with renewable energy sources[J]. Control Theory & Applications, 2016, 33(12): 1555-1561.
[5]	国家发展改革委, 国家能源局, 财政部, 等. 关于印发北方地区冬季清洁取暖规划(2017—2021年)的通知[EB/OL].(2017-12-20)[2023-03-31]. https://www.ndrc.gov.cn/xxgk/zcfb/tz/201712/t20171220_962623.html.
[6]	央广网. 天津“煤改电”工程全部竣工惠及46万户居民[EB/OL].(2019-11-07)[2023-03-31]. http://www.cnr.cn/tj/jrtj/20191107/t20191107_524848423.shtml.
[7]	央视新闻. 北京清洁取暖 “煤改电”用户已达130万[EB/OL].(2020-11-21)[2023-03-31]. https://www.thecover.cn/news/6126854.
[8]	王德宇, 宋亚岚. 通州区“煤改电”智能化监测平台建设实践[J]. 广播电视网络, 2021, 28(5):57-59.
	WANG Deyu, SONG Yalan. Construction practice of intelligent monitoring platform for "Replacing Coal with Electricity" in Tongzhou district[J]. Radio & Television Network, 2021, 28(5): 57-59.
[9]	王昆, 刘洋. 河北:“光伏+电采暖”让农民清洁温暖过冬[EB/OL].(2021-12-01)[2023-03-31]. https://hebei.ifeng.com/c/8BZcVDCDQ6I.
[10]	祁兵, 张露露, 李彬, 等. 基于电压监测的分布式电采暖实时优化控制策略[J]. 现代电力, 2021, 38(4): 449-454.
	QI Bing, ZHANG Lulu, LI Bin, et al. A Voltage monitoring-based realtime optimized control strategy for distributed electric heating[J]. Modern Electric Power, 2021, 38(4): 449-454.
[11]	严干贵, 阚天洋, 杨玉龙, 等. 基于深度强化学习的分布式电采暖参与需求响应优化调度[J]. 电网技术, 2020, 44(11):4140-4147.
	YAN Gangui, KAN Tianyang, YANG Yulong, et al. Demand response optimal scheduling for distributed electric heating based on deep reinforcement learning[J]. Power Grid Technology, 2020, 44(11): 4140-4147.
[12]	曾建电, 王田, 贾维嘉, 等. 传感云研究综述[J]. 计算机研究与发展, 2017, 54(5):925-939.
	ZENG Jiandian, WANG Tian, JIA Weijia, et al. A survey on sensor-cloud[J]. Journal of Computer Research and Development, 2017, 54(5):925-939.
[13]	张依琳, 陈宇翔, 田晖, 等. 联邦学习在边缘计算场景中应用研究进展[J]. 小型微型计算机统, 2021, 42(12):2645-2653.
	ZHANG Yilin, CHEN Yuxiang, TIAN Hui, et al. Survey on federated learning application on scenarios of edge computing[J]. Journal of Chinese Computer Systems, 2021, 42(12): 2645-2653.
[14]	范帅, 郏琨琪, 郭炳庆, 等. 分散式电采暖负荷协同优化运行策略[J]. 电力系统自动化, 2017, 41(19):20-29.
	FAN Shuai, JIA Kunqi, GUO Bingqing, et al. Collaborative optimal operation strategy for decentralized electric heating loads[J]. Power System Automation, 2017, 41(19):20-29.
[15]	李香龙, 马龙飞, 赵向阳, 等. 基于LSTM网络的时间多尺度电采暖负荷预测[J]. 电力系统及其自动化学报, 2021, 33(4):71-75.
	LI Xianglong, MA Longfei, ZHAO Xiangyang, et al. Multi-time scale electric heating load forecasting based on long short-term memory network[J]. Proceedings of the CSU-EPSA, 2021, 33(4): 71-75.
[16]	肖勇, 吴昊文, 王宗义, 等. 面向可中断负荷控制的需求响应通信业务优化[J]. 电力系统自动化, 2020, 44(15):36-43.
	XIAO Yong, WU Haowen, WANG Zongyi, et al. Communication service optimization of demand response for interruptible load control[J]. Power System Automation, 2020, 44(15): 36-43.
[17]	李光辉, 李宜璟, 胡世红. 移动边缘计算中基于联邦学习的视频请求预测和协作缓存策略[J]. 电子与信息学报, 2023, 45(1):218-226.
	LI Guanghui, LI Yijing, HU Shihong. Video request prediction and cooperative caching strategy based on federated learning in mobile edge computing[J]. Journal of Electronics & Information Technology, 2023, 45(1):218-226.
[18]	CHENG K, FAN T, JIN Y, et al. Secureboost: A lossless federated learning framework[J]. IEEE Intelligent Systems, 2021, 36(6): 87-98.
[19]	AONO Y, HAYASHI T, WANG L, et al. Privacy-preserving deep learning via additively homomorphic encryption[J]. IEEE Transactions on Information Forensics and Security, 2017, 13(5): 1333-1345. doi: 10.1109/TIFS.2017.2787987
[20]	NASR M, SHOKRI R, HOUMANSADR A. Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning[C]// 2019 IEEE symposium on security and privacy (SP). IEEE, 2019: 739-753.
[21]	BONAWITZ K, IVANOV V, KREUTER B, et al. Practical secure aggregation for privacy-preserving machine learning[C]// Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. 2017: 1175-1191.
[22]	刘艺璇, 陈红, 刘宇涵, 等. 联邦学习中的隐私保护技术[J]. 软件学报, 2022, 33(3):1057-1092.
	LIU Yixuan, CHEN Hong, LIU Yuhan, et al. Privacy-preserving techniques in federated learning[J]. Journal of Software, 2022, 33(3): 1057-1092.
[23]	ZHU T, LI G, ZHOU W, et al. Differentially private data publishing and analysis: A survey[J]. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(8): 1619-1638. doi: 10.1109/TKDE.2017.2697856
[24]	GEYER R C, KLEIN T, NABI M. Differentially private federated learning: A client level perspective[J]. arXiv preprint arXiv:1712.07557, 2017.
[25]	LU Y, HUANG X, DAI Y, et al. Differentially private asynchronous federated learning for mobile edge computing in urban informatics[J]. IEEE Transactions on Industrial Informatics, 2019, 16(3): 2134-2143. doi: 10.1109/TII.9424
[26]	ARACHCHIGE P C M, BERTOK P, KHALIL I, et al. Local differential privacy for deep learning[J]. IEEE Internet of Things Journal, 2019, 7(7): 5827-5842. doi: 10.1109/JIoT.6488907
[27]	WANG J, WU L, WANG H, et al. An efficient and privacy-preserving outsourced support vector machine training for internet of medical things[J]. IEEE Internet of Things Journal, 2020, 8(1): 458-473. doi: 10.1109/JIoT.6488907
[28]	王腾, 霍峥, 黄亚鑫, 等. 联邦学习中的隐私保护技术研究综述[J]. 计算机应用, 2023, 43(2):437-449. doi: 10.11772/j.issn.1001-9081.2021122072
	WANG Teng, HUO Zheng, HUANG Yaxin, et al. Review on privacy-preserving technologies in federated learning[J]. Journal of Computer Applications, 2023, 43(2):437-449. doi: 10.11772/j.issn.1001-9081.2021122072
[29]	陈智罡, 王箭, 宋新霞. 全同态加密研究[J]. 计算机应用研究, 2014, 31(6):1624-1631.
	CHEN Zhigang, WANG Jian, SONG Xinxia. Survey on fully homomorphic encryption[J]. Journal of Computer Applications, 2014, 31(6): 1624-1631. doi: 10.3724/SP.J.1087.2011.01624
[30]	SHAMIR A. How to share a secret[J]. Communications of the ACM, 1979, 22(11): 612-613.. doi: 10.1145/359168.359176
[31]	BONAWITZ K, IVANOV V, KREUTER B, et al. Practical secure aggregation for privacy-preserving machine learning[C]// Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. 2017: 1175-1191.
[32]	KONEČNÝ J. Stochastic,distributed and federated optimization for machine learning[J]. arXiv preprint arXiv:1707.01155, 2017.
[33]	LU S, ZHANG Y, WANG Y, et al. Learn electronic health records by fully decentralized federated learning[J]. arXiv preprint arXiv:1912.01792, 2019.
[34]	BRENDAN MCMAHAN H, MOORE E, RAMAGE D, et al. Communication-efficient learning of deep networks from decentralized data[J]. arXiv e-prints, 2016: arXiv: 1602.05629.
[35]	SATTLER F, WIEDEMANN S, MÜLLER K R, et al. Robust and communication-efficient federated learning from Non-IID data[J]. IEEE transactions on neural networks and learning systems, 2019, 31(9): 3400-3413. doi: 10.1109/TNNLS.5962385
[36]	高晗, 田育龙, 许封元, 等. 深度学习模型压缩与加速综述[J]. 软件学报, 2021, 32(1):68-92.
	GAO Han, TIAN Yulong, XU Fengyuan, et al. Survey of deep learning model compression and acceleration[J]. Journal of Software, 2021, 32(1): 68-92.
[37]	ZHU Z, HONG J, ZHOU J. Data-free knowledge distillation for heterogeneous federated learning[C]// International Conference on Machine Learning. PMLR, 2021: 12878-12889.
[38]	KONEČNÝ J, MCMAHAN H B, RAMAGE D, et al. Federated optimization: Distributed machine learning for on-device intelligence[J]. arXiv preprint arXiv:1610.02527, 2016.
[39]	SATTLER F, MARBAN A, RISCHKE R, et al. Communication-efficient federated distillation[J]. arXiv preprint arXiv:2012.00632, 2020.
[40]	MILLS J, HU J, MIN G. Communication-efficient federated learning for wireless edge intelligence in IoT[J]. IEEE Internet of Things Journal, 2019, 7(7): 5986-5994. doi: 10.1109/JIoT.6488907
[41]	LIU L, ZHANG J, SONG S H, et al. Client-edge-cloud hierarchical federated learning[C]// ICC 2020—2020 IEEE International Conference on Communications (ICC). IEEE, 2020: 1-6.
[42]	JIANG Y, KONEČNÝ J, RUSH K, et al. Improving federated learning personalization via model agnostic meta learning[J]. arXiv preprint arXiv:1909.12488, 2019.
[43]	SMITH V, CHIANG C K, SANJABI M, et al. Federated multi-task learning[J]. Advances in Neural Information Processing Systems, 2017, 30.
[44]	WANG X, HAN Y, WANG C, et al. In-edge AI: Intelligentizing mobile edge computing, caching and communication by federated learning[J]. IEEE Network, 2019, 33(5): 156-165.
[45]	WU Q, HE K, CHEN X. Personalized federated learning for intelligent IoT applications: A cloud-edge based framework[J]. IEEE Open Journal of the Computer Society, 2020, 1: 35-44. doi: 10.1109/OJCS
[46]	LI D, WANG J. FedMD: Heterogenous federated learning via model distillation[J]. arXiv preprint arXiv:1910.03581, 2019.
[47]	CHEN Y, QIN X, WANG J, et al. Fedhealth: A federated transfer learning framework for wearable healthcare[J]. IEEE Intelligent Systems, 2020, 35(4): 83-93.
[48]	FINN C, ABBEEL P, LEVINE S. Model-agnostic meta-learning for fast adaptation of deep networks[C]// International Conference on Machine Learning. PMLR, 2017: 1126-1135.
[49]	PENG Z, XU J, CHU X, et al. Vfchain: Enabling verifiable and auditable federated learning via blockchain systems[J]. IEEE Transactions on Network Science and Engineering, 2021, 9(1): 173-186. doi: 10.1109/TNSE.2021.3050781
[50]	ZHANG Y, LIU L, GU Y, et al. Offloading in software defined network at edge with information asymmetry: A contract theoretical approach[J]. Journal of Signal Processing Systems, 2016, 83: 241-253. doi: 10.1007/s11265-015-1038-9
[51]	KHAN L U, PANDEY S R, TRAN N H, et al. Federated learning for edge networks: Resource optimization and incentive mechanism[J]. IEEE Communications Magazine, 2020, 58(10): 88-93.
[52]	周全兴, 李秋贤, 丁红发, 等. 基于博弈论优化的高效联邦学习方案[J]. 计算机工程, 2022, 48(8):144-151,159. doi: 10.19678/j.issn.1000-3428.0062413
	ZHOU Quanxing, LI Qiuxian, DING Hongfa, et al. Efficient federated learning scheme based on game theory optimization[J]. Computer Engineering, 2022, 48(8):144-151,159. doi: 10.19678/j.issn.1000-3428.0062413
[53]	莫梓嘉, 高志鹏, 杨杨, 等. 面向车联网数据隐私保护的高效分布式模型共享策略[J]. 通信学报, 2022, 43(4):83-94. doi: 10.11959/j.issn.1000-436x.2022074
	MO Zijia, GAO Zhipeng, YANG Yang, et al. Efficient distributed model sharing strategy for data privacy protection in Internet of vehicles[J]. Journal on Communications, 2022, 43(4): 83-94. doi: 10.11959/j.issn.1000-436x.2022074
[54]	王鑫, 周泽宝, 余芸, 等. 一种面向电能量数据的联邦学习可靠性激励机制[J]. 计算机科学, 2022, 49(3):31-38. doi: 10.11896/jsjkx.210700195
	WANG Xin, ZHOU Zebao, YU Yun, et al. Reliable incentive mechanism for federated learning of electric metering data[J]. Computer Science, 2022, 49(3): 31-38. doi: 10.11896/jsjkx.210700195
[55]	朱建明, 张沁楠, 高胜, 等. 基于区块链的隐私保护可信联邦学习模型[J]. 计算机学报, 2021, 44(12):2464-2484.
	ZHU Jianming, ZHANG Qinnan, GAO Sheng, et al. Privacy preserving and trustworthy federated learning model based on blockchain[J]. Chinese Journal of Computers, 2021, 44(12): 2464-2484.