{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T13:47:40Z","timestamp":1767016060818,"version":"3.28.0"},"reference-count":10,"publisher":"IEEE","license":[{"start":{"date-parts":[[2020,2,1]],"date-time":"2020-02-01T00:00:00Z","timestamp":1580515200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,2,1]],"date-time":"2020-02-01T00:00:00Z","timestamp":1580515200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,2,1]],"date-time":"2020-02-01T00:00:00Z","timestamp":1580515200000},"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":[[2020,2]]},"DOI":"10.1109\/icaiic48513.2020.9065270","type":"proceedings-article","created":{"date-parts":[[2020,4,17]],"date-time":"2020-04-17T01:46:17Z","timestamp":1587087977000},"page":"388-391","source":"Crossref","is-referenced-by-count":6,"title":["Power Demand Forecasting Using Long Short-Term Memory Neural Network based Smart Grid"],"prefix":"10.1109","author":[{"given":"Van Hoa","family":"Nguyen","sequence":"first","affiliation":[]},{"given":"Van","family":"Bui","sequence":"additional","affiliation":[]},{"given":"Joongheon","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Yeong Min","family":"Jang","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"doi-asserted-by":"publisher","key":"ref4","DOI":"10.3390\/en11010161"},{"year":"0","author":"olah","journal-title":"LSTM Networks","key":"ref3"},{"doi-asserted-by":"publisher","key":"ref10","DOI":"10.1016\/j.enbuild.2015.01.008"},{"key":"ref6","first-page":"52","volume":"103","author":"yaslan","year":"2017","journal-title":"Empirical mode decomposition based denoising method with support vector regression for time series prediction A case study for electricity load forecasting"},{"doi-asserted-by":"publisher","key":"ref5","DOI":"10.1016\/j.rser.2015.03.035"},{"year":"0","author":"yan","journal-title":"Understanding lstms and its diagrams","key":"ref8"},{"doi-asserted-by":"publisher","key":"ref7","DOI":"10.1016\/j.segan.2016.02.005"},{"doi-asserted-by":"publisher","key":"ref2","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref9","first-page":"332","volume":"47","author":"fumo","year":"2015","journal-title":"Regression analysis for prediction of residential energy consumption Renew Sustain Energy Rev"},{"doi-asserted-by":"publisher","key":"ref1","DOI":"10.1016\/j.rser.2013.10.022"}],"event":{"name":"2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","start":{"date-parts":[[2020,2,19]]},"location":"Fukuoka, Japan","end":{"date-parts":[[2020,2,21]]}},"container-title":["2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9046688\/9064864\/09065270.pdf?arnumber=9065270","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,27]],"date-time":"2022-06-27T20:15:49Z","timestamp":1656360949000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9065270\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2]]},"references-count":10,"URL":"https:\/\/doi.org\/10.1109\/icaiic48513.2020.9065270","relation":{},"subject":[],"published":{"date-parts":[[2020,2]]}}}