{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T16:09:15Z","timestamp":1783526955576,"version":"3.55.0"},"reference-count":28,"publisher":"IEEE","license":[{"start":{"date-parts":[[2019,6,1]],"date-time":"2019-06-01T00:00:00Z","timestamp":1559347200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2019,6,1]],"date-time":"2019-06-01T00:00:00Z","timestamp":1559347200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,6,1]],"date-time":"2019-06-01T00:00:00Z","timestamp":1559347200000},"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":[[2019,6]]},"DOI":"10.1109\/cec.2019.8789968","type":"proceedings-article","created":{"date-parts":[[2019,8,8]],"date-time":"2019-08-08T20:00:07Z","timestamp":1565294407000},"page":"1510-1516","source":"Crossref","is-referenced-by-count":57,"title":["Particle Swarm Optimization-based CNN-LSTM Networks for Forecasting Energy Consumption"],"prefix":"10.1109","author":[{"given":"Tae-Young","family":"Kim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sung-Bae","family":"Cho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref10","first-page":"3460","article-title":"Speeding up automata networks by extrapolation of learning curves","volume":"15","author":"domhan","year":"2015","journal-title":"IJCAI"},{"key":"ref11","first-page":"2546","article-title":"Algorithms for hyper-parameter optimization","author":"bergstra","year":"2011","journal-title":"Advances in neural information processing systems"},{"key":"ref12","first-page":"281","article-title":"Random search for hyper-parameter optimization","author":"bergstra","year":"2012","journal-title":"Journal of Machine Learning Research"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/3071178.3071229"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/IWCMC.2017.7986470"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.3390\/en10101525"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2004.09.006"},{"key":"ref17","first-page":"1","article-title":"Optimizing deep learning hyperparameters through an evolutionary algorithm","author":"young","year":"2015","journal-title":"Proceedings of the Workshop on Machine Learning in High-Performance Computing Environments"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2015.71"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCB.2005.847740"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008388118869"},{"key":"ref4","first-page":"271","article-title":"Seasonal decomposition of electricity consumption data","volume":"6","author":"ahmad","year":"2017","journal-title":"Rev Integr Bus Econ Res"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/PL00011652"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2007.01.023"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.04.032"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.apenergy.2014.12.019","article-title":"Modeling and forecasting energy consumption for heterogeneous buildings using a physical– statistical approach","volume":"144","author":"l\u00fc","year":"2015","journal-title":"Applied Energy"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-03493-1_50"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2582924"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2017.02.085"},{"key":"ref9","first-page":"2342","article-title":"An empirical exploration of recurrent network architectures","author":"jozefowicz","year":"2015","journal-title":"Int Conf on Machine Learning"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2017.12.049"},{"key":"ref20","doi-asserted-by":"crossref","DOI":"10.3390\/en11071636","article-title":"Optimal deep learning LSTM model for electric load forecasting using feature selection and genetic algorithm: Comparison with machine learning approaches","author":"bouktif","year":"2018","journal-title":"Energies"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2014.12.029"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2015.09.002"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/s12273-012-0059-6"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2014.07.078"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2018.04.004"},{"key":"ref25","first-page":"339","article-title":"Prediction of electricity consumption based on genetic algorithm-RBF neural network","volume":"5","author":"zang","year":"2010","journal-title":"Int Conf Advanced Computer Control"}],"event":{"name":"2019 IEEE Congress on Evolutionary Computation (CEC)","location":"Wellington, New Zealand","start":{"date-parts":[[2019,6,10]]},"end":{"date-parts":[[2019,6,13]]}},"container-title":["2019 IEEE Congress on Evolutionary Computation (CEC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8778428\/8789744\/08789968.pdf?arnumber=8789968","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T10:48:46Z","timestamp":1658141326000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8789968\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6]]},"references-count":28,"URL":"https:\/\/doi.org\/10.1109\/cec.2019.8789968","relation":{},"subject":[],"published":{"date-parts":[[2019,6]]}}}