{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T21:24:14Z","timestamp":1730237054293,"version":"3.28.0"},"reference-count":20,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,10,13]],"date-time":"2021-10-13T00:00:00Z","timestamp":1634083200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,10,13]],"date-time":"2021-10-13T00:00:00Z","timestamp":1634083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,10,13]],"date-time":"2021-10-13T00:00:00Z","timestamp":1634083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,10,13]]},"DOI":"10.1109\/icct52962.2021.9657948","type":"proceedings-article","created":{"date-parts":[[2022,1,4]],"date-time":"2022-01-04T15:36:39Z","timestamp":1641310599000},"page":"1046-1051","source":"Crossref","is-referenced-by-count":0,"title":["Short-Term Power Load Probability Density Forecasting Based on a Double-Layer LSTM-Attention Quantile Regression"],"prefix":"10.1109","author":[{"given":"Xiaofeng","family":"Tao","sequence":"first","affiliation":[]},{"given":"Yang","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Xueliang","family":"Yang","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","first-page":"3061","article-title":"Short-term public building load probability density forecasting based on correlation analysis and long-and short- term memory network quantile regression","volume":"43","author":"yang","year":"2019","journal-title":"Power System Technology"},{"key":"ref11","first-page":"24","article-title":"Short-term Bus Load Probability Density Forecasting Based on CNN-GRU Quantile Regression","volume":"48","author":"zang","year":"2020","journal-title":"Smart Power Safety"},{"key":"ref12","first-page":"708","article-title":"Ocean Eddy Detection Model Based on Deep Convolution Neural Network","volume":"52","author":"zhang","year":"2020","journal-title":"Journal of NanJing University of Aeronautics and Astronautics"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICC40277.2020.9148738"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2900481"},{"key":"ref15","first-page":"812","article-title":"Dynamic Personalized Search based on RNN with attention mechanism","volume":"43","author":"zhou","year":"2020","journal-title":"Chinese Journal of Computers"},{"key":"ref16","first-page":"1","article-title":"Attention in convolutional LSTM for gesture recognition","author":"zhang","year":"2018","journal-title":"Advances in neural information processing systems"},{"key":"ref17","first-page":"1","article-title":"Neural machine translation by jointly learning to align and translate","author":"bahdanau","year":"0","journal-title":"International Conference on Learning Representations"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2017.2749413"},{"key":"ref19","first-page":"787","article-title":"Uncertain trajectory forecasting method using non-parametric density estimation","volume":"45","author":"cheng","year":"2019","journal-title":"Acta automatica ainica"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2015.11.011"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICCT.2018.8600009"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1002\/1099-131X(200007)19:4<299::AID-FOR775>3.3.CO;2-M"},{"key":"ref5","first-page":"1","article-title":"Short-term consumer load probability density forecasting based on EMD-QRF","volume":"47","author":"yang","year":"2019","journal-title":"Power system protection and control"},{"key":"ref8","first-page":"768","article-title":"Short-term power load probability density forecasting method based on real time price and support vector quantile regression","volume":"37","author":"he","year":"0","journal-title":"Proceedings of the CSEE"},{"key":"ref7","first-page":"93","article-title":"A power load probability density forecasting method based on RBF neural network quantile regression","volume":"33","author":"he","year":"0","journal-title":"Proceedings of the CSEE"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM42981.2021.9488816"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2014.2364233"},{"key":"ref9","first-page":"5238","article-title":"Month-ahead wind power curve probabilistic forecasting based on factor analysis and quantile regression neural network","volume":"37","author":"li","year":"0","journal-title":"Proceedings of the CSEE"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-9473(00)00046-3"}],"event":{"name":"2021 IEEE 21st International Conference on Communication Technology (ICCT)","start":{"date-parts":[[2021,10,13]]},"location":"Tianjin, China","end":{"date-parts":[[2021,10,16]]}},"container-title":["2021 IEEE 21st International Conference on Communication Technology (ICCT)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9657830\/9657831\/09657948.pdf?arnumber=9657948","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T12:56:26Z","timestamp":1652187386000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9657948\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,13]]},"references-count":20,"URL":"https:\/\/doi.org\/10.1109\/icct52962.2021.9657948","relation":{},"subject":[],"published":{"date-parts":[[2021,10,13]]}}}