{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T11:16:11Z","timestamp":1781522171542,"version":"3.54.1"},"reference-count":71,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T00:00:00Z","timestamp":1679356800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T00:00:00Z","timestamp":1679356800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1007\/s13042-023-01796-8","type":"journal-article","created":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T04:37:17Z","timestamp":1679373437000},"page":"129-148","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Enhanced neighborhood node graph neural networks for load forecasting in smart grid"],"prefix":"10.1007","volume":"15","author":[{"given":"Jiang","family":"Yanmei","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liu","family":"Mingsheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Yangyang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liu","family":"Yaping","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhang","family":"Jingyun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liu","family":"Yifeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liu","family":"Chunyang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,3,21]]},"reference":[{"issue":"6","key":"1796_CR1","doi-asserted-by":"publisher","first-page":"5141","DOI":"10.1109\/JIOT.2018.2838574","volume":"5","author":"FX Tang","year":"2019","unstructured":"Tang FX, Fadlullah ZM, Mao BM, Kato N (2019) An Intelligent Traffic Load Prediction-Based Adaptive Channel Assignment Algorithm in SDN-IoT: A Deep Learning Approach. IEEE Internet Things J 5(6):5141\u20135154. https:\/\/doi.org\/10.1109\/JIOT.2018.2838574","journal-title":"IEEE Internet Things J"},{"issue":"10","key":"1796_CR2","doi-asserted-by":"publisher","first-page":"9449","DOI":"10.1007\/s12652-020-02685-6","volume":"12","author":"AH Rabie","year":"2021","unstructured":"Rabie AH, Saleh AI, Ali HA (2021) Smart electrical grids based on cloud. IoT, and big data technologies: state of the art, ournal of ambient intelligence and humanized computing. 12(10):9449\u20139480. https:\/\/doi.org\/10.1007\/s12652-020-02685-6","journal-title":"IoT, and big data technologies: state of the art, ournal of ambient intelligence and humanized computing."},{"issue":"3","key":"1796_CR3","doi-asserted-by":"publisher","first-page":"1835","DOI":"10.1109\/TCOMM.2020.3043760","volume":"69","author":"I Maity","year":"2021","unstructured":"Maity I, Misra S, Mandal C (2021) CORE: Prediction-Based Control Plane Load Reduction in Software-Defined IoT Networks. IEEE Trans Commun 69(3):1835\u20131844. https:\/\/doi.org\/10.1109\/TCOMM.2020.3043760","journal-title":"IEEE Trans Commun"},{"issue":"6","key":"1796_CR4","doi-asserted-by":"publisher","first-page":"4912","DOI":"10.1109\/JIOT.2020.2975847","volume":"7","author":"G Bedi","year":"2020","unstructured":"Bedi G, Venayagamoorthy GK, Singh R (2020) Development of an IoT-Driven Building Environment for Prediction of Electric Energy Consumption. IEEE Internet Things J 7(6):4912\u20134921. https:\/\/doi.org\/10.1109\/JIOT.2020.2975847","journal-title":"IEEE Internet Things J"},{"key":"1796_CR5","doi-asserted-by":"publisher","DOI":"10.1007\/s11265-022-01785-0","author":"L Randall","year":"2022","unstructured":"Randall L, Agrawal P, Mohapatra A (2022) IoT Based Load Forecasting for Reliable Integration of Renewable Energy Sources. JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY. https:\/\/doi.org\/10.1007\/s11265-022-01785-0","journal-title":"JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY."},{"issue":"9","key":"1796_CR6","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3390\/electronics10091026","volume":"10","author":"V Bui","year":"2022","unstructured":"Bui V, Le NT, Nguyen VH, Kim J, Jang YM (2022) Multi-Behavior with Bottleneck Features LSTM for Load Forecasting in Building Energy Management System. Electronics 10(9):209\u2013236. https:\/\/doi.org\/10.3390\/electronics10091026","journal-title":"Electronics"},{"key":"1796_CR7","doi-asserted-by":"publisher","unstructured":"Zhang CG, Chen ZC, Wu LJ, Cheng, SY, Lin PJ (2019) A NB-IoT based intelligent combiner box for PV arrays integrated with short-term power prediction using extreme learning machine and similar days, IOP Conference Series-Earth and Environmental Science. Paper presented at 4th International Conference on Energy Engineering and Environmental Protection (EEEP), 2021, Xiamen, PEOPLES R CHINA. 467. (2019). https:\/\/doi.org\/10.1088\/1755-1315\/467\/1\/012081","DOI":"10.1088\/1755-1315\/467\/1\/012081"},{"key":"1796_CR8","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2022.3204361","author":"XY Zhang","year":"2022","unstructured":"Zhang XY, Cordoba-Pachon JR, Guo PQ, Watkins C, Kuenzel S (2022) Privacy-Preserving Federated Learning for Value-Added Service Model in Advanced Metering Infrastructure. IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS. https:\/\/doi.org\/10.1109\/TCSS.2022.3204361","journal-title":"IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS."},{"key":"1796_CR9","doi-asserted-by":"publisher","unstructured":"Liang F, Yu, A, Hatcher WG, Yu W, Lu C (2019) Deep Learning-Based Power Usage Forecast Modeling and Evaluation, Procedia Computer Science. Paper presented at 9th International Conference of Information and Communication Technology [ICICT], Nanning, PEOPLES R CHINA. https:\/\/doi.org\/10.1016\/j.procs.2019.06.016","DOI":"10.1016\/j.procs.2019.06.016"},{"issue":"9","key":"1796_CR10","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/j.apenergy.2017.04.039","volume":"198","author":"LY Xiao","year":"2017","unstructured":"Xiao LY, Shao W, Yu MX, Ma J, Jin CJ (2017) Research and application of a hybrid wavelet neural network model with the improved cuckoo search algorithm for electrical power system forecasting. Appl Energy 198(9):203\u2013222. https:\/\/doi.org\/10.1016\/j.apenergy.2017.04.039","journal-title":"Appl Energy"},{"key":"1796_CR11","unstructured":"Chandrarathna Kasun, Edalati Arman, Tabar Ahmad Reza Fourozan (2020) Forecasting Short-term load using Econometrics time series model with T-student Distribution. Preprint at https:\/\/arxiv.org\/Statistical Finance"},{"key":"1796_CR12","unstructured":"Ozturk Ali, Tosun Salih, Celik Hasan (2016) Forecasting Short-term load using Econometrics time series model with T-student Distribution. International Symposium Innovative Technologies Engineering and Science"},{"key":"1796_CR13","doi-asserted-by":"publisher","unstructured":"Wu F, Cattani C, Song W, Zio E (2020) Fractional ARIMA with an improved cuckoo search optimization for the efficient Short-term power load forecasting. Academie PressAEJ - Alexandria Engineering Journal. 59(5). https:\/\/doi.org\/10.1016\/j.aej.2020.06.049","DOI":"10.1016\/j.aej.2020.06.049"},{"key":"1796_CR14","doi-asserted-by":"publisher","unstructured":"Wang Huiping, Wang Yi (2022) Forecasting solar energy consumption using a fractional discrete grey model with time power term. Clean Technologies and Environmental Policy. Preprint at https:\/\/doi.org\/10.1007\/s10098-022-02320-2","DOI":"10.1007\/s10098-022-02320-2"},{"key":"1796_CR15","doi-asserted-by":"publisher","unstructured":"Zhu Fuyun, Wu Guoqing (2021) Load Forecasting of the Power System: An Investigation Based on the Method of Random Forest Regression. Energy Engineering. Preprint at https:\/\/doi.org\/10.32604\/EE.2021.015602","DOI":"10.32604\/EE.2021.015602"},{"key":"1796_CR16","doi-asserted-by":"publisher","unstructured":"Aasim Singh SN, Mohapatra A (2021) Data driven day-ahead electrical load forecasting through repeated wavelet transform assisted SVM model. Applied Soft Computing. 111(16):107730. https:\/\/doi.org\/10.1016\/j.asoc.2021.107730","DOI":"10.1016\/j.asoc.2021.107730"},{"key":"1796_CR17","doi-asserted-by":"publisher","unstructured":"Lv Da, Xue Jianjie, Zhang Yan, Tang Mengcong, Chen Qian (2020) Research on Power User Intelligent Load Forecasting Method Based on Data State Drive. Journal of Physics Conference Series. 1449:012073. https:\/\/doi.org\/10.1088\/1742-6596\/1449\/1\/012073","DOI":"10.1088\/1742-6596\/1449\/1\/012073"},{"key":"1796_CR18","doi-asserted-by":"publisher","first-page":"546","DOI":"10.1016\/j.procs.2020.02.026","volume":"166","author":"H Ma","year":"2020","unstructured":"Ma H, Tang JM (2020) Short-Term Load Forecasting of Microgrid Based on Chaotic Particle Swarm Optimization. Procedia Computer Science. 166:546\u2013550. https:\/\/doi.org\/10.1016\/j.procs.2020.02.026","journal-title":"Procedia Computer Science."},{"issue":"2","key":"1796_CR19","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/j.jup.2021.101294","volume":"73","author":"GF Fan","year":"2021","unstructured":"Fan GF, Yu M, Dong SQ, Yeh YH, Hong WC (2021) Forecasting short-term electricity load using hybrid support vector regression with grey catastrophe and random forest modeling. Utilities Policy. 73(2):173\u2013196. https:\/\/doi.org\/10.1016\/j.jup.2021.101294","journal-title":"Utilities Policy."},{"key":"1796_CR20","doi-asserted-by":"publisher","unstructured":"Hafeez G, Khan I, Usman M, Aurangzeb K, Ullah A (2020) Fast and Accurate Hybrid Electric Load Forecasting with Novel Feature Engineering and Optimization Framework in Smart Grid. Paper presented at the 6th Conference on Data Science and Machine Learning Applications (CDMA), Riyadh, Saudi Arabia. https:\/\/doi.org\/10.1016\/j.jup.2021.101294","DOI":"10.1016\/j.jup.2021.101294"},{"issue":"4281219","key":"1796_CR21","doi-asserted-by":"publisher","first-page":"4281219","DOI":"10.1155\/2020\/4281219","volume":"2020","author":"S Carcangiu","year":"2020","unstructured":"Carcangiu S, Fanni A, Pegoraro PA, Sias G, Sulis S (2020) Forecasting-Aided Monitoring for the Distribution System State Estimation. Complexity 2020(4281219):4281219. https:\/\/doi.org\/10.1155\/2020\/4281219","journal-title":"Complexity"},{"key":"1796_CR22","doi-asserted-by":"publisher","unstructured":"Peng H, Li J, Song Y, Yang R, He L (2021) Streaming Social Event Detection and Evolution Discovery in Heterogeneous Information Networks, ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA. 15. https:\/\/doi.org\/10.1145\/3447585","DOI":"10.1145\/3447585"},{"key":"1796_CR23","doi-asserted-by":"crossref","unstructured":"Peng Hao, Li Jianxin, Gong Qiran, Ning Yuanxin, He Lifang (2020) Motif-Matching Based Subgraph-Level Attentional Convolutional Network for Graph Classification. Paper presented at 34th AAAI Conference on Artificial Intelligence \/ 32nd Innovative Applications of Artificial Intelligence Conference \/ 10th AAAI Symposium on Educational Advances in Artificial Intelligence, New York, NY. 34:5387-5394","DOI":"10.1609\/aaai.v34i04.5987"},{"key":"1796_CR24","doi-asserted-by":"publisher","unstructured":"Silva Mad, Abreu T, CR Santos-J\u00fanior, Minussi CR (2021) Load forecasting for smart grid based on continuous-learning neural network. Electric Power Systems Research. 201(4281219):107545-107545. https:\/\/doi.org\/10.1016\/j.epsr.2021.107545","DOI":"10.1016\/j.epsr.2021.107545"},{"key":"1796_CR25","doi-asserted-by":"publisher","unstructured":"Soares LD, Franco, Emc (2021) BiGRU-CNN neural network applied to short-term electric load forecasting. Electric Power Systems Research. 32. https:\/\/doi.org\/10.1590\/0103-6513.20210087","DOI":"10.1590\/0103-6513.20210087"},{"key":"1796_CR26","doi-asserted-by":"publisher","unstructured":"Miao Kai, Hua Qiang, Shi Huifeng (2021) Short-Term Load Forecasting Based on CNN-BiLSTM with Bayesian Optimization and Attention Mechanism. CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 32. https:\/\/doi.org\/10.1002\/cpe.6676","DOI":"10.1002\/cpe.6676"},{"key":"1796_CR27","doi-asserted-by":"publisher","unstructured":"Zhao H, Yao Q, Weiwei TU (2021) Search to aggregate neighborhood for graph neural network, IEEE International Conference on Data Engineering. Paper presented at 2021 IEEE 37TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2021), ELECTR NETWORKc. https:\/\/doi.org\/10.1109\/ICDE51399.2021.00054","DOI":"10.1109\/ICDE51399.2021.00054"},{"key":"1796_CR28","unstructured":"Dudek G, Peka P, Smyl S (2020) A Hybrid Residual Dilated LSTM end Exponential Smoothing Model for Mid-Term Electric Load Forecasting. Preprint at https:\/\/arxiv.org\/2004.00508"},{"issue":"1","key":"1796_CR29","first-page":"231","volume":"41","author":"Y Xiao","year":"2021","unstructured":"Xiao Y, Zheng KH, Zheng ZJ, Qian B, Li S, Ma QL (2021) Multi-scale skip deep long short-term memory network for short-term multivariate load forecasting. Journal of Computer Applications. 41(1):231\u2013236","journal-title":"Journal of Computer Applications."},{"key":"1796_CR30","doi-asserted-by":"crossref","unstructured":"Fu L (2020) Time Series-oriented Load Prediction Using Deep Peephole LSTM. Paper presented at 12th International Conference on Advanced Computational Intelligence (ICACI), Dali, PEOPLES R CHINA","DOI":"10.1109\/ICACI49185.2020.9177688"},{"issue":"3","key":"1796_CR31","doi-asserted-by":"publisher","first-page":"1371","DOI":"10.1007\/s00202-020-00930-x","volume":"10","author":"XZ Xu","year":"2020","unstructured":"Xu XZ, Meng ZR (2020) A hybrid transfer learning model for short-term electric load forecasting. Electr Eng 10(3):1371\u20131381. https:\/\/doi.org\/10.1007\/s00202-020-00930-x","journal-title":"Electr Eng"},{"key":"1796_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2020.110658","volume":"233","author":"YK Lu","year":"2020","unstructured":"Lu YK, Tian Z, Zhou RY, Liu WJ (2020) Multi-step-ahead prediction of thermal load in regional energy system using deep learning method. ENERGY AND BUILDINGS. 233:110658. https:\/\/doi.org\/10.1016\/j.enbuild.2020.110658","journal-title":"ENERGY AND BUILDINGS."},{"key":"1796_CR33","doi-asserted-by":"publisher","first-page":"7414318","DOI":"10.1155\/2019\/7414318","volume":"2019","author":"B Wang","year":"2019","unstructured":"Wang B, Zhang LM, Ma HR, Wang HX, Wan SH (2019) Parallel LSTM-Based Regional Integrated Energy System Multienergy Source-Load Information Interactive Energy Prediction. Complexity 2019:7414318. https:\/\/doi.org\/10.1155\/2019\/7414318","journal-title":"Complexity"},{"key":"1796_CR34","doi-asserted-by":"publisher","first-page":"121423","DOI":"10.1016\/j.jclepro.2020.121423","volume":"266","author":"B Wang","year":"2020","unstructured":"Wang B, Zhang LM, Ma HR, Wang HX, Wan SH (2020) Smart load prediction analysis for distributed power network of Holiday Cabins in Norwegian rural area. J Clean Prod 266:121423. https:\/\/doi.org\/10.1016\/j.jclepro.2020.121423","journal-title":"J Clean Prod"},{"key":"1796_CR35","doi-asserted-by":"publisher","first-page":"102363","DOI":"10.1016\/j.scs.2020.102363","volume":"62","author":"B Wang","year":"2020","unstructured":"Wang B, Zhang LM, Ma HR, Wang HX, Wan SH (2020) Edge sensing data-imaging conversion scheme of load forecasting in smart grid. SUSTAINABLE CITIES AND SOCIETY. 62:102363. https:\/\/doi.org\/10.1016\/j.scs.2020.102363","journal-title":"SUSTAINABLE CITIES AND SOCIETY."},{"issue":"1","key":"1796_CR36","doi-asserted-by":"publisher","first-page":"6332","DOI":"10.1038\/s41598-020-63331-x","volume":"10","author":"Zhao Yong","year":"2020","unstructured":"Yong Zhao, Jiangxuehui Zhao (2020) Residual resistance prediction of sailing yacht based on random forest model, Journal of Huazhong University of Science and Technology. Nature Science. 10(1):6332. https:\/\/doi.org\/10.1038\/s41598-020-63331-x","journal-title":"Nature Science."},{"issue":"11","key":"1796_CR37","first-page":"118","volume":"49","author":"L Fang","year":"2021","unstructured":"Fang L, Zhou ZY, Hong YP (2021) Symmetry Analysis of the Uncertain Alternative Box-Cox Regression Model. SYMMETRY-BASEL. 49(11):118\u2013122","journal-title":"SYMMETRY-BASEL."},{"key":"1796_CR38","doi-asserted-by":"publisher","first-page":"108967","DOI":"10.1016\/j.ymssp.2022.108967","volume":"172","author":"Z Zhao","year":"2022","unstructured":"Zhao Z, Zhao YG, Li PP (2022) Efficient approach for dynamic reliability analysis based on uniform design method and Box-Cox transformation. MECHANICAL SYSTEMS AND SIGNAL PROCESSING. 172:108967. https:\/\/doi.org\/10.1016\/j.ymssp.2022.108967","journal-title":"MECHANICAL SYSTEMS AND SIGNAL PROCESSING."},{"key":"1796_CR39","doi-asserted-by":"publisher","unstructured":"Zhu KD, Geng J, Wang K (2021) A hybrid prediction model based on pattern sequence-based matching method and extreme gradient boosting for holiday load forecasting. ELECTRIC POWER SYSTEMS RESEARCH. 190(106841). https:\/\/doi.org\/10.1016\/j.epsr.2020.106841","DOI":"10.1016\/j.epsr.2020.106841"},{"key":"1796_CR40","doi-asserted-by":"publisher","unstructured":"Ge L, Li Y, Yan J, Wang Y, Zhang N (2021) Short-term Load Prediction of Integrated Energy System with Wavelet Neural Network Model Based on Improved Particle Swarm Optimization and Chaos Optimization Algorithm. JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY. 9(6):1490-1499. https:\/\/doi.org\/10.35833\/MPCE.2020.000647","DOI":"10.35833\/MPCE.2020.000647"},{"key":"1796_CR41","doi-asserted-by":"publisher","unstructured":"Chen Jinpeng, Hu Zhijian, Chen Weinan, Gao Mingxin, Du Yixing, Lin Mingrong (2021) Load Prediction of Integrated Energy System Based on Combination of Quadratic Modal Decomposition and Deep Bidirectional Long Short-term Memory and Multiple Linear Regression. Automation of Electric Power Systems. 45(1000-1026):85-94. https:\/\/doi.org\/10.35833\/MPCE.2020.000647","DOI":"10.35833\/MPCE.2020.000647"},{"key":"1796_CR42","doi-asserted-by":"publisher","unstructured":"Guo Wei, Zhang Kai, Wei Xinjie, Liu Mei (2021) Short-Term Load Forecasting Method Based on Deep Reinforcement Learning for Smart Grid. Hindawi Limited. 2021(8453896). https:\/\/doi.org\/10.1155\/2021\/8453896","DOI":"10.1155\/2021\/8453896"},{"key":"1796_CR43","doi-asserted-by":"publisher","unstructured":"Peng Hao, Zhang Ruitong, Li Shaoning, Cao Yuwei, Pan Shirui, Yu Philip (2022) Reinforced, Incremental and Cross-lingual Event Detection From Social Messages. IEEE transactions on pattern analysis and machine intelligence. PP. (2022). https:\/\/doi.org\/10.1109\/TPAMI.2022.3144993","DOI":"10.1109\/TPAMI.2022.3144993"},{"key":"1796_CR44","doi-asserted-by":"publisher","unstructured":"Liu Chao, Li Xinchuan, Zhao Dongyang, Guo Shaolong, Yao Hong (2020) A-GNN: Anchors-Aware Graph Neural Networks for Node Embedding, Lecture Notes of the Institute for Computer Sciences Social Informatics and Telecommunications Engineering. Paper presented at 15th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness (QShine), Shenzhen, PEOPLES R CHINA. 300:141-153. https:\/\/doi.org\/10.1007\/978-3-030-38819-5_9","DOI":"10.1007\/978-3-030-38819-5_9"},{"issue":"4","key":"1796_CR45","doi-asserted-by":"publisher","first-page":"2434","DOI":"10.1109\/TII.2021.3093115","volume":"2434\u20132442","author":"Jianhua Dai","year":"2022","unstructured":"Dai Jianhua, Chen Yuanmeng, Xiao Lin, Jia Lei, He Yongjun (2022) Design and Analysis of a Hybrid GNN-ZNN Model with a Fuzzy Adaptive Factor for Matrix Inversion. IEEE Trans Industr Inf 2434\u20132442(4):2434\u20132442. https:\/\/doi.org\/10.1109\/TII.2021.3093115","journal-title":"IEEE Trans Industr Inf"},{"key":"1796_CR46","doi-asserted-by":"crossref","unstructured":"Zhao Xusheng, Dai Qiong, Wu Jia, Peng Hao, Liu Mingsheng, Bai Xu, Tan Jianlong, Wang Senzhang, Yu Philip (2022) TinyGNN: Multi-view Tensor Graph Neural Networks Through Reinforced Aggregation, IEEE Transactions on Knowledge and Data Engineering","DOI":"10.1109\/TKDE.2022.3142179"},{"issue":"1","key":"1796_CR47","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1109\/LCA.2020.2988991","volume":"19","author":"Z Zhang","year":"2020","unstructured":"Zhang Z, Leng J, Ma L, Miao Y, Guo M (2020) Architectural Implications of Graph Neural Networks. IEEE Comput Archit Lett 19(1):59\u201362. https:\/\/doi.org\/10.1109\/LCA.2020.2988991","journal-title":"IEEE Comput Archit Lett"},{"issue":"3","key":"1796_CR48","doi-asserted-by":"publisher","first-page":"628","DOI":"10.1109\/TC.2021.3057082","volume":"71","author":"H Peng","year":"2022","unstructured":"Peng H, Yang R, Wang Z, Li J, He L, Yu P, Zomaya A, Ranjan R (2022) LIME: Low-Cost Incremental Learning for Dynamic Heterogeneous Information Networks. IEEE Trans Comput 71(3):628\u2013642. https:\/\/doi.org\/10.1109\/TC.2021.3057082","journal-title":"IEEE Trans Comput"},{"issue":"10","key":"1796_CR49","doi-asserted-by":"publisher","first-page":"2541","DOI":"10.1109\/TPDS.2021.3065737","volume":"32","author":"Youhui Bai","year":"2020","unstructured":"Bai Youhui, Li Cheng, Lin Zhiqi, Wu Yufei, Xu Yinlong (2020) Efficient Data Loader for Fast Sampling-based GNN Training on Large Graphs. IEEE Trans Parallel Distrib Syst 32(10):2541\u20132556. https:\/\/doi.org\/10.1109\/TPDS.2021.3065737","journal-title":"IEEE Trans Parallel Distrib Syst"},{"key":"1796_CR50","doi-asserted-by":"publisher","unstructured":"Guirado R, Jain A, Abadal S, Alarc\u00f3n E (2019) Characterizing the Communication Requirements of GNN Accelerators: A Model-Based Approach, IEEE International Symposium on Circuits and Systems. Paper presented at2021 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS), Daegu, SOUTH KOREA. https:\/\/doi.org\/10.1109\/ISCAS51556.2021.9401612","DOI":"10.1109\/ISCAS51556.2021.9401612"},{"key":"1796_CR51","doi-asserted-by":"publisher","unstructured":"Yin JB, Wang YY, Chen KY (2021) A Novel Graph Based Sequence Forecasting Model for Electric Load of Campus. Paper presented at 2nd International Conference on Artificial Intelligence and Information Systems (ICAIIS ), Chongqing, PEOPLES R CHINA, 28-30 MAY. https:\/\/doi.org\/10.1155\/2021\/8453896","DOI":"10.1155\/2021\/8453896"},{"key":"1796_CR52","doi-asserted-by":"publisher","unstructured":"Liao W, Bak-Jensen B, Pillai JR, Wang Y, Wang Y (2022) A Review of Graph Neural Networks and Their Applications in Power Systems. JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY. 10:345-360. https:\/\/doi.org\/10.35833\/MPCE.2021.000058","DOI":"10.35833\/MPCE.2021.000058"},{"key":"1796_CR53","doi-asserted-by":"publisher","unstructured":"Li HJ (2022) Short-Term Wind Power Prediction via Spatial Temporal Analysis and Deep Residual Networks. FRONTIERS IN ENERGY RESEARCH. 10(920407). https:\/\/doi.org\/10.3389\/fenrg.2022.920407","DOI":"10.3389\/fenrg.2022.920407"},{"key":"1796_CR54","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1016\/j.ins.2021.07.007","volume":"578","author":"H Peng","year":"2021","unstructured":"Peng H, Du B, Liu M, Liu M, He L (2021) Dynamic Graph Convolutional Network for Long-Term Traffic Flow Prediction with Reinforcement Learning. Inf Sci 578:401\u2013416. https:\/\/doi.org\/10.1016\/j.ins.2021.07.007","journal-title":"Inf Sci"},{"issue":"4","key":"1796_CR55","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/10.1145\/3490181","volume":"40","author":"H Peng","year":"2022","unstructured":"Peng H, Zhang R, Dou Y, Yang R, Yu PS (2022) Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks. ACM TRANSACTIONS ON INFORMATION SYSTEMS. 40(4):69. https:\/\/doi.org\/10.1016\/10.1145\/3490181","journal-title":"ACM TRANSACTIONS ON INFORMATION SYSTEMS."},{"key":"1796_CR56","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.neunet.2022.06.035","volume":"154","author":"X Zhao","year":"2022","unstructured":"Zhao X, Wu J, Peng H, Beheshti A, Monaghan J, Mcalpine D, Hernandez-Perez H, Dras M, Dai Q, Li Y (2022) Deep Reinforcement Learning Guided Graph Neural Networks for Brain Network Analysis. Neural networks\u202f: the official journal of the International Neural Network Society. 154:56\u201367. https:\/\/doi.org\/10.1016\/j.neunet.2022.06.035","journal-title":"Neural networks : the official journal of the International Neural Network Society."},{"key":"1796_CR57","doi-asserted-by":"publisher","first-page":"1585","DOI":"10.1109\/TIE.2018.2808918","volume":"66","author":"Y Zhang","year":"2018","unstructured":"Zhang Y, Xiong R, He H, Pecht M (2018) Lithium-ion battery remaining useful life prediction with Box-Cox transformation and Monte Carlo simulation. IEEE Trans Industr Electron 66:1585\u20131597. https:\/\/doi.org\/10.1109\/TIE.2018.2808918","journal-title":"IEEE Trans Industr Electron"},{"key":"1796_CR58","doi-asserted-by":"publisher","unstructured":"Johannesen NJ, Kolhe ML, Goodwin M (2020) Smart load prediction analysis for distributed power network of Holiday Cabins in Norwegian rural area-ScienceDirect. Journal of Cleaner Production. 266(121423). https:\/\/doi.org\/10.1016\/j.jclepro.2020.121423","DOI":"10.1016\/j.jclepro.2020.121423"},{"key":"1796_CR59","doi-asserted-by":"publisher","unstructured":"Jiang YM, Liu MS, Peng H, Bhuiyan MZA (2021) A reliable deep learning-based algorithm design for IoT load identification in smart grid. Ad Hoc Networks. 123:102643. https:\/\/doi.org\/10.1016\/j.adhoc.2021.102643","DOI":"10.1016\/j.adhoc.2021.102643"},{"key":"1796_CR60","doi-asserted-by":"crossref","unstructured":"Wang HX, Li YF, Li YF, Dang LM, Ko J, Han D, Moon H (2020) Smartphone-based bulky waste classification using convolutional neural networks, MULTIMEDIA TOOLS AND APPLICATIONS. 79(39-40):29411-29431. https:\/\/doi.org\/29411-29431","DOI":"10.1007\/s11042-020-09571-5"},{"issue":"2","key":"1796_CR61","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1007\/s00371-020-02043-9","volume":"38","author":"R Unlu","year":"2020","unstructured":"Unlu R, Kiris R (2020) Detection of damaged buildings after an earthquake with convolutional neural networks in conjunction with image segmentation. VISUAL COMPUTER. 38(2):685\u2013694. https:\/\/doi.org\/10.1007\/s00371-020-02043-9","journal-title":"VISUAL COMPUTER."},{"key":"1796_CR62","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1007\/978-3-030-60239-0_12","volume":"12453","author":"R Unlu","year":"2020","unstructured":"Unlu R, Kiris R (2020) Neural Network Compression and Acceleration by Federated Pruning. Lecture Notes Artificial Intelligence 12453:173\u2013183. https:\/\/doi.org\/10.1007\/978-3-030-60239-0_12","journal-title":"Lecture Notes Artificial Intelligence"},{"key":"1796_CR63","unstructured":"Shan Lin, Hong Wang, Linhai Qi, Hanyu Feng, Su Ying (2021) Short-term Load Forecasting Based on Conditional Generative Adversarial Network. Automation of Electric Power Systems. 45(1000\u20131026):52\u201360"},{"key":"1796_CR64","unstructured":"Han Kai, Wang Yunhe, Guo Jianyun, Tang Yehui, Wu Enhua (2022) Vision GNN: An Image is Worth Graph of Nodes. arXiv preprint arXiv:2206.00272"},{"key":"1796_CR65","doi-asserted-by":"crossref","unstructured":"Zhang Ruitong, Peng Hao, Dou Yingtong, Wu Jia, Sun Qingyun, Zhang Jingyi, Yu Philip S (2022) Automating DBSCAN via Deep Reinforcement Learning. In Proceedings of The 31th ACM International Conference on Information and Knowledge Management, CIKM 2022, Atlanta, Georgia, USA, 17-22 Oct","DOI":"10.1145\/3511808.3557245"},{"key":"1796_CR66","doi-asserted-by":"crossref","unstructured":"Peng Hao, Li Jianxin, Wang Zheng, Yang Renyu, Liu Mingsheng, Zhang Mingming, Yu Philip, He Lifang (2021) Lifelong Property Price Prediction: A Case Study for the Toronto Real Estate Market. IEEE Transactions on Knowledge and Data Engineering","DOI":"10.1109\/TKDE.2021.3112749"},{"key":"1796_CR67","unstructured":"Hai W, Wang, Y.: Load Forecast of Gas Region Based on ARIMA Algorithm, Chinese Control and Decision Conference. 1960\u20131965. (2020) Paper presented at 32nd Chinese Control And Decision Conference (CCDC). PEOPLES R CHINA, Hefei, p 2020"},{"key":"1796_CR68","doi-asserted-by":"publisher","first-page":"4547","DOI":"10.3934\/mbe.2022210","volume":"19","author":"XQ Dai","year":"2022","unstructured":"Dai XQ, Sheng KC, Shu FZ (2022) Ship power load forecasting based on PSO-SVM. MATHEMATICAL BIOSCIENCES AND ENGINEERING. 19:4547\u20134567. https:\/\/doi.org\/10.3934\/mbe.2022210","journal-title":"MATHEMATICAL BIOSCIENCES AND ENGINEERING."},{"issue":"1001\u20139081","key":"1796_CR69","first-page":"231","volume":"41","author":"Xiao Yong","year":"2021","unstructured":"Yong Xiao, Kaihong Zheng, Zhenjing Zheng, Bin Qian, Sen Li, Qianli Ma (2021) Multi-scale skip deep long short-term memory network for short-term multivariate load forecasting. MATHEMATICAL BIOSCIENCES AND ENGINEERING. 41(1001\u20139081):231\u2013236","journal-title":"MATHEMATICAL BIOSCIENCES AND ENGINEERING."},{"issue":"1000\u20131026","key":"1796_CR70","first-page":"52","volume":"45","author":"Shan Lin","year":"2021","unstructured":"Lin Shan, Wang Hong, Qi Linhai, Feng Hanyu, Su Ying (2021) Short-term Load Forecasting Based on Conditional Generative Adversarial Network. Automation of Electric Power Systems. 45(1000\u20131026):52\u201360","journal-title":"Automation of Electric Power Systems."},{"key":"1796_CR71","doi-asserted-by":"publisher","unstructured":"Liu RW, Liang MH, Nie JT, Yuan YL, Xiong ZH, Yu H, Guizani N (2022) STMGCN: Mobile Edge Computing-Empowered Vessel Trajectory Prediction Using Spatio-Temporal Multigraph Convolutional Network. Automation of Electric Power Systems. 18(11):7977-7987. https:\/\/doi.org\/10.1109\/TII.2022.3165886","DOI":"10.1109\/TII.2022.3165886"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-01796-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-023-01796-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-01796-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,6]],"date-time":"2024-01-06T08:21:43Z","timestamp":1704529303000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-023-01796-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,21]]},"references-count":71,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["1796"],"URL":"https:\/\/doi.org\/10.1007\/s13042-023-01796-8","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,21]]},"assertion":[{"value":"30 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 January 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 March 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}