{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T15:27:55Z","timestamp":1780500475096,"version":"3.54.1"},"reference-count":34,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,3]]},"DOI":"10.1109\/ciss.2017.7926112","type":"proceedings-article","created":{"date-parts":[[2017,5,15]],"date-time":"2017-05-15T20:34:28Z","timestamp":1494880468000},"page":"1-6","source":"Crossref","is-referenced-by-count":126,"title":["Electric load forecasting in smart grids using Long-Short-Term-Memory based Recurrent Neural Network"],"prefix":"10.1109","author":[{"family":"Jian Zheng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"family":"Cencen Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"family":"Ziang Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"family":"Xiaohua Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1111\/1467-9876.00109"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2010.2080325"},{"key":"ref31","first-page":"115","article-title":"Learning precise timing with lstm recurrent networks","volume":"3","author":"gers","year":"2002","journal-title":"Journal of Machine Learning Research"},{"key":"ref30","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/978-3-642-24797-2_3","article-title":"Neural networks","author":"graves","year":"2012","journal-title":"Supervised Sequence Labelling with Recurrent Neural Networks"},{"key":"ref34","author":"adhikari","year":"2013","journal-title":"An Introductory Study on Time Series Modeling and Forecasting CoRR"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.5120\/13146-0553"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.enpol.2009.10.007"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/MCS.2007.904656"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.4304\/jsw.7.6.1273-1280"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2012.11.015"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/S0169-2070(02)00057-2"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"338","DOI":"10.21437\/Interspeech.2014-80","article-title":"Long short-term memory recurrent neural network architectures for large scale acoustic modeling","author":"sak","year":"2014","journal-title":"InterSpeech"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2012.01.039"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2008.02.042"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/72.279181"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/S0925-2312(01)00702-0"},{"key":"ref27","first-page":"1310","article-title":"On the difficulty of training recurrent neural networks","volume":"28","author":"pascanu","year":"2013","journal-title":"ICML"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.aej.2011.01.015"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2005.11.027"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ASRU.2013.6707742"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"2664","DOI":"10.1016\/j.asoc.2010.10.015","article-title":"A novel hybridization of artificial neural networks and arima models for time series forecasting","volume":"11","author":"khashei","year":"2011","journal-title":"Applied Soft Computing"},{"key":"ref8","article-title":"Stationary and non-stationary time series prediction using state space model and pattern-based approach","author":"kam","year":"2014","journal-title":"The University of Texas at Arlington"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/PMAPS.2006.360237"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.3390\/en7031576"},{"key":"ref9","first-page":"1673","article-title":"Analysis of nonstationary nonlinear economic time series of gold price: A comparative study","volume":"5","author":"lineesh","year":"2010","journal-title":"International Mathematical Forum"},{"key":"ref1","first-page":"45","article-title":"Short term load forecasting using time series analysis: A case study for karnataka, india","volume":"1","author":"nataraja","year":"2012","journal-title":"International Journal of Engineering Science and Innovative Technology"},{"key":"ref20","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1111\/j.2517-6161.1961.tb00424.x","article-title":"Prediction by exponentially weighted moving averages and related methods","author":"cox","year":"1961","journal-title":"Journal of the Royal Statistical Society Series B (Methodological)"},{"key":"ref22","first-page":"145","article-title":"Long term time series prediction with multi-input multi-output local learning","author":"bontempi","year":"2008","journal-title":"Proc 2nd ESTSP"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ACOMP.2015.24"},{"key":"ref24","author":"lipton","year":"2015","journal-title":"A critical review of recurrent neural networks for sequence learning"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2009.11.030"},{"key":"ref26","article-title":"Recurrent neural networks","author":"medsker","year":"2001","journal-title":"Design and Applications"},{"key":"ref25","first-page":"3104","article-title":"Sequence to sequence learning with neural networks","author":"sutskever","year":"2014","journal-title":"Advances in neural information processing systems"}],"event":{"name":"2017 51st Annual Conference on Information Sciences and Systems (CISS)","location":"Baltimore, MD, USA","start":{"date-parts":[[2017,3,22]]},"end":{"date-parts":[[2017,3,24]]}},"container-title":["2017 51st Annual Conference on Information Sciences and Systems (CISS)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/7917217\/7926061\/07926112.pdf?arnumber=7926112","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T16:27:25Z","timestamp":1750264045000},"score":1,"resource":{"primary":{"URL":"http:\/\/ieeexplore.ieee.org\/document\/7926112\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,3]]},"references-count":34,"URL":"https:\/\/doi.org\/10.1109\/ciss.2017.7926112","relation":{},"subject":[],"published":{"date-parts":[[2017,3]]}}}