Telecommunications Science ›› 2022, Vol. 38 ›› Issue (5): 114-123.doi: 10.11959/j.issn.1000-0801.2022074
• Research and Development • Previous Articles Next Articles
Wenjun ZHU, Sining WANG, Xiaoxin GAO, Qian ZHENG
Revised:
2022-03-14
Online:
2022-05-20
Published:
2022-05-01
CLC Number:
Wenjun ZHU, Sining WANG, Xiaoxin GAO, Qian ZHENG. Load forecasting based on knowledge flow and transfer learning[J]. Telecommunications Science, 2022, 38(5): 114-123.
[1] | Internet of things industry perspective[EB]. 2021. |
[2] | 周峰, 周晖, 刁赢龙 . 泛在电力物联网智能感知关键技术发展思路[J]. 中国电机工程学报, 2020,40(1): 70-82,375. |
ZHOU F , ZHOU H , DIAO Y L . Development of intelligent perception key technology in the ubiquitous Internet of Things in electricity[J]. Proceedings of the CSEE, 2020,40(1): 70-82,375. | |
[3] | 张宁, 马国明, 关永刚 ,等. 全景信息感知及智慧电网[J]. 中国电机工程学报, 2021,41(4): 1274-1283,1535. |
ZHANG N , MA G M , GUAN Y G ,et al. Panoramic information perception and intelligent grid[J]. Proceedings of the CSEE, 2021,41(4): 1274-1283,1535. | |
[4] | 汤凌晗 . 物联网信息感知与交互技术[J]. 信息记录材料, 2020,21(11): 218-219. |
TANG L H . Internet of things information perception and inte-raction technology[J]. Information Recording Materials, 2020,21(11): 218-219. | |
[5] | 雷煜卿, 仝杰, 张树华 ,等. 能源互联网感知层技术标准体系研究[J]. 供用电, 2021,38(7): 14-20,33. |
LEI Y Q , TONG J , ZHANG S H ,et al. Research on technical standard system of energy Internet sensing layer[J]. Distribution& Utilization, 2021,38(7): 14-20,33. | |
[6] | 薛澜, 姜李丹, 黄颖 ,等. 资源异质性、知识流动与产学研协同创新:以人工智能产业为例[J]. 科学学研究, 2019,37(12): 2241-2251. |
XUE L , JIANG L D , HUANG Y ,et al. Resource heterogeneity,knowledge flow and synergy innovation of industry-university-research institute:an empirical study on AI industry[J]. Studies in Science of Science, 2019,37(12): 2241-2251. | |
[7] | 丁晟春, 刘梦露, 傅柱 . 概念设计中基于知识流的多维设计知识统一建模技术研究[J]. 数据分析与知识发现, 2018,2(2): 11-19. |
DING S C , LIU M L , FU Z . Unified multidimensional model based on knowledge flow in conceptual design[J]. Data Analy-sis and Knowledge Discovery, 2018,2(2): 11-19. | |
[8] | 王继业, 蒲天骄, 仝杰 ,等. 能源互联网智能感知技术框架与应用布局[J]. 电力信息与通信技术, 2020,18(4): 1-14. |
WANG J Y , PU T J , TONG J ,et al. Intelligent perception tech-nology framework and application layout of energy Internet[J]. Electric Power Information and Communication Technology, 2020,18(4): 1-14. | |
[9] | 万灿, 崔文康, 宋永华 . 新能源电力系统概率预测:基本概念与数学原理[J]. 中国电机工程学报, 2021,41(19): 6493-6509. |
WAN C , CUI W K , SONG Y H . Probabilistic forecasting for power systems with renewable energy sources:basic concepts and mathematical principles[J]. Proceedings of the CSEE, 2021,41(19): 6493-6509. | |
[10] | 杨挺, 赵黎媛, 王成山 . 人工智能在电力系统及综合能源系统中的应用综述[J]. 电力系统自动化, 2019,43(1): 2-14. |
YANG T , ZHAO L Y , WANG C S . Review on application of ar-tificial intelligence in power system and integrated energy sys-tem[J]. Automation of Electric Power Systems, 2019,43(1): 2-14. | |
[11] | 田楚杰 . 神经网络在时间序列与时空序列流量预测中的应用与研究[D]. 北京:北京邮电大学, 2021. |
TIAN C J . The research of temporal flow prediction and spa-tial-temporal flow prediction based on neural networks[D]. Bei-jing:Beijing University of Posts and Telecommunications, 2021. | |
[12] | 叶琳, 杨滢, 洪道鉴 ,等. 深度学习在电力系统中的应用研究综述[J]. 浙江电力, 2019,38(5): 83-89. |
YE L , YANG Y , HONG D J ,et al. A survey of deep learning technology application in power system[J]. Zhejiang Electric Power, 2019,38(5): 83-89. | |
[13] | 蔡秋娜, 苏炳洪, 闫斌杰 ,等. 基于参数迁移的节假日短期负荷预测方法[J]. 电气自动化, 2020,42(4): 59-60,98. |
CAI Q N , SU B H , YAN B J ,et al. Forecasting method for hol-iday short-term load based on parameter transfer[J]. Electrical Automation, 2020,42(4): 59-60,98. | |
[14] | 孙晓燕, 李家钊, 曾博 ,等. 基于特征迁移学习的综合能源系统小样本日前电力负荷预测[J]. 控制理论与应用, 2021,38(1): 63-72. |
SUN X Y , LI J Z , ZENG B ,et al. Small-sample day-ahead power load forecasting of integrated energy system based on feature transfer learning[J]. Control Theory & Applications, 2021,38(1): 63-72. | |
[15] | 张勇, 陶一凡, 巩敦卫 . 迁移学习引导的变源域长短时记忆网络建筑负荷预测[J]. 控制与决策, 2021,36(10): 2328-2338. |
ZHANG Y , TAO Y F , GONG D W . Load forecasting of build-ings using LSTM based on transfer learning with variable source domain[J]. Control and Decision, 2021,36(10): 2328-2338. | |
[16] | 史凯钰, 张东霞, 韩肖清 ,等. 基于 LSTM 与迁移学习的光伏发电功率预测数字孪生模型[J]. 电网技术, 2022,46(4): 1363-1372. |
SHI K Y , ZHANG D X , HAN X Q ,et al. Digital twin model of photovoltaic power generation prediction based on LSTM and transfer learning[J]. Power System Technology, 2022,46(4): 1363-1372. | |
[17] | 杨秀, 吴吉海, 孙改平 ,等. 基于深度学习和迁移学习的公共楼宇非侵入式负荷分解[J]. 电网技术, 2022,46(3): 1160-1169. |
YANG X , WU J H , SUN G P ,et al. Non-intrusive load decom-position of public buildings based on deep learning and transfer learning[J]. Power System Technology, 2022,46(3): 1160-1169. | |
[18] | LEE E , RHEE W . Individualized short-term electric load forecasting with deep neural network based transfer learning and meta learning[J]. IEEE Access, 2021(9): 15413-15425. |
[19] | PAN S J , YANG Q . A survey on transfer learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2010,22(10): 1345-1359. |
[20] | ZHUANG F Z , QI Z Y , DUAN K Y ,et al. A comprehensive survey on transfer learning[J]. Proceedings of the IEEE, 2021,109(1): 43-76. |
[21] | LU J , BEHBOOD V , HAO P ,et al. Transfer learning using computational intelligence:a survey[J]. Knowledge-Based Systems, 2015(80): 14-23. |
[22] | WU Z H , JIANG H K , ZHAO K ,et al. An adaptive deep transfer learning method for bearing fault diagnosis[J]. Measurement, 2020(151): 107227. |
[23] | MANDIC D P , REHMAN N U , WU Z H ,et al. Empirical mode decomposition-based time-frequency analysis of multivariate signals:the power of adaptive data analysis[J]. IEEE Signal Processing Magazine, 2013,30(6): 74-86. |
[24] | 聂品磊, 费东, 王宏杰 ,等. 基于 EMD-BP 神经网络的短期电力负荷预测[J]. 化工自动化及仪表, 2016,43(3): 305-307,332. |
NIE P L , FEI D , WANG H J ,et al. Short-term power load fore-casting based on EMD-BP neural network[J]. Control and In-struments in Chemical Industry, 2016,43(3): 305-307,332. | |
[25] | 彭晋, 王昕, 孟小楠 . 多方数据融合挖掘安全与隐私保护参考架构研究[J]. 信息技术与标准化, 2021(8): 36-39. |
PENG J , WANG X , MENG X N . Research on security and pri-vacy-preserving reference architecture for multi-party data fu-sion and mining[J]. Information Technology & Standardization, 2021(8): 36-39. | |
[26] | LIU M , LI D D , LI Q K ,et al. An online intelligent method to calibrate radar and camera sensors for data fusing[J]. Journal of Physics:Conference Series, 2020,1631(1): 012183. |
[27] | 李志刚 . 基于特征融合的异构数据分类方法研究[D]. 济南:齐鲁工业大学, 2021. |
LI Z G . Research on the classification method of heterogeneous data based on feature fusion[D]. Jinan:Qilu University of Technology, 2021. | |
[28] | 任泽裕, 王振超, 柯尊旺 ,等. 多模态数据融合综述[J]. 计算机工程与应用, 2021,57(18): 49-64. |
REN Z Y , WANG Z C , KE Z W ,et al. Survey of multimodal data fusion[J]. Computer Engineering and Applications, 2021,57(18): 49-64. | |
[29] | VERGARA C , MARTIN R K , COLLINS P J ,et al. Multi-sensor data fusion between radio tomographic imaging and noise radar[J]. IET Radar,Sonar & Navigation, 2020,14(2): 187-193. |
[30] | 单政博, 张勇, 吴小刚 ,等. 基于数据融合的数字化电网调度指挥和管控平台[J]. 机械与电子, 2021,39(6): 44-47. |
SHAN Z B , ZHANG Y , WU X G ,et al. Digital power grid dis-patching command and control platform based on data fusion[J]. Machinery & Electronics, 2021,39(6): 44-47. | |
[31] | 陈红, 肖建红, 刘文婕 ,等. 电能计量中的数据融合分析[J]. 集成电路应用, 2021,38(6): 42-43. |
CHEN H , XIAO J H , LIU W J ,et al. Analysis of data fusion in electric energy measurement[J]. Application of IC, 2021,38(6): 42-43. | |
[32] | 李俊双 . 基于深度学习的数据融合在空气质量监测的研究与应用[D]. 北京:中国科学院大学(中国科学院沈阳计算技术研究所), 2021. |
LI J S . Research and application of data fusion based on deep learning in air quality monitoring[D]. Beijing:Institute of Computing Technology,Chinese Academy of Sciences, 2021. | |
[33] | 董真杰, 郑琛瑶, 张国龙 . 不同精度数据融合的自适应加权平均法研究[J]. 舰船电子工程, 2014,34(10): 31-33,126. |
DONG Z J , ZHENG C Y , ZHANG G L . Self-adaption of weighted average research for data fusion with different preci-sion[J]. Ship Electronic Engineering, 2014,34(10): 31-33,126. | |
[34] | 王美蕴 . 基于多无线传感器的数据融合算法[J]. 电子测试, 2021(8): 73-74. |
WANG M Y . Data fusion algorithm based on multiple wireless sensors[J]. Electronic Test, 2021(8): 73-74. | |
[35] | 黄婷婷, 冯锋 . 无线传感器网络异构数据融合模型优化研究[J]. 计算机科学, 2020,47(S2): 339-344. |
HUANG T T , FENG F . Study on optimization of heterogeneous data fusion model in wireless sensor network[J]. Computer Science, 2020,47(S2): 339-344. |
[1] | Haoshuang LIU, Yong ZHANG, Yingbo CAO. Substructure correlation adaptation transfer learning method based on K-means clustering [J]. Telecommunications Science, 2023, 39(3): 124-134. |
[2] | Yingzhao ZHU. Review on heterogeneous transfer learning [J]. Telecommunications Science, 2020, 36(3): 100-110. |
[3] | Can LIU,Junna SHANG,Ruijiang LI,Keqiang YUE. Indoor dynamic environment lo calization algorithm based on transfer learning [J]. Telecommunications Science, 2018, 34(8): 98-108. |
[4] | Wenzhao WU,Xiao WANG,Kunpeng ZHAO,Yafeng WEN. Indoor localization algorithm based on RFID technology for smart park [J]. Telecommunications Science, 2016, 32(3): 187-191. |
[5] | Kang Chen,Yong Xiang,Chao Yu. New Trend of Machine Learning in the Age of Big Data [J]. Telecommunications Science, 2012, 28(12): 77-85. |
[6] | Zhao Gansen,Yu Hai,Ji Tongkai and Song Hong. Adaptive Resource Provisioning for Cloud Computing [J]. Telecommunications Science, 2012, 28(1): 31-37. |
[7] | Gansen Zhao,Hai Yu,Tongkai Ji,Hong Song. Adaptive Resource Provisioning for Cloud Computing [J]. Telecommunications Science, 2012, 28(1): 35-41. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||
|