{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T10:24:37Z","timestamp":1777631077455,"version":"3.51.4"},"reference-count":50,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2022,8,26]],"date-time":"2022-08-26T00:00:00Z","timestamp":1661472000000},"content-version":"vor","delay-in-days":237,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2019M3F2A1073179"],"award-info":[{"award-number":["2019M3F2A1073179"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2022,1]]},"abstract":"<jats:p>\n                    Over the decades, a rapid upsurge in electricity demand has been observed due to overpopulation and technological growth. The optimum production of energy is mandatory to preserve it and improve the energy infrastructure using the power load forecasting (PLF) method. However, the complex energy systems\u2019 transition towards more robust and intelligent system will ensure its momentous role in the industrial and economical world. The extraction of deep knowledge from complex energy data patterns requires an efficient and computationally intelligent deep learning\u2010based method to examine the future electricity demand. Stand by this, we propose an intelligent deep learning\u2010based PLF method where at first the data collected from the house through meters are fed into the pre\u2010assessment step. Next, the sequence of refined data is passed into a modified convolutional long short\u2010term memory (ConvLSTM) network that captures the spatiotemporal correlations from the sequence and generates the feature maps. The generated feature map is forward propagated into a deep gated recurrent unit (GRU) network for learning, which provides the final PLF. We experimentally proved that the proposed method revealed promising results using mean square error (MSE) and root mean square error (RMSE) and outperformed state of the art using the competitive power load dataset.(Github Code). (Github code:\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/FathUMinUllah3797\/ConvLSTM-Deep_GRU\">https:\/\/github.com\/FathUMinUllah3797\/ConvLSTM-Deep_GRU<\/jats:ext-link>\n                    ).\n                  <\/jats:p>","DOI":"10.1155\/2022\/2993184","type":"journal-article","created":{"date-parts":[[2022,8,26]],"date-time":"2022-08-26T13:35:07Z","timestamp":1661520907000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Deep Learning\u2010Assisted Short\u2010Term Power Load Forecasting Using Deep Convolutional LSTM and Stacked GRU"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1243-9358","authenticated-orcid":false,"given":"Fath U Min","family":"Ullah","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7538-2689","authenticated-orcid":false,"given":"Amin","family":"Ullah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7531-3827","authenticated-orcid":false,"given":"Noman","family":"Khan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8139-7091","authenticated-orcid":false,"given":"Mi Young","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1936-6785","authenticated-orcid":false,"given":"Seungmin","family":"Rho","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6678-7788","authenticated-orcid":false,"given":"Sung Wook","family":"Baik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2022,8,26]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/1510257"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.seta.2021.101940"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/7414318"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/5571539"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2018.08.027"},{"key":"e_1_2_10_6_2","unstructured":"Woo-hyunS. Moon\u2019s nuclear-free policy lauded but face technical hurdles 2017 http:\/\/www.koreaherald.com\/view.php?ud=20170623000670."},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2016.10.079"},{"key":"e_1_2_10_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2020.113559"},{"key":"e_1_2_10_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2016.08.081"},{"key":"e_1_2_10_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2015.11.068"},{"key":"e_1_2_10_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2017.12.054"},{"key":"e_1_2_10_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2017.07.114"},{"key":"e_1_2_10_13_2","doi-asserted-by":"publisher","DOI":"10.3390\/math9060611"},{"key":"e_1_2_10_14_2","doi-asserted-by":"publisher","DOI":"10.3390\/math9243326"},{"key":"e_1_2_10_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2015.02.017"},{"key":"e_1_2_10_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2014.12.008"},{"key":"e_1_2_10_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2018.07.026"},{"key":"e_1_2_10_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2017.11.060"},{"key":"e_1_2_10_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2018.03.125"},{"key":"e_1_2_10_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2017.12.002"},{"key":"e_1_2_10_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2018.10.004"},{"key":"e_1_2_10_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.buildenv.2012.04.021"},{"key":"e_1_2_10_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2015.10.002"},{"key":"e_1_2_10_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2019.03.078"},{"key":"e_1_2_10_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2015.03.035"},{"key":"e_1_2_10_26_2","doi-asserted-by":"publisher","DOI":"10.1002\/int.22537"},{"key":"e_1_2_10_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2019.02.052"},{"key":"e_1_2_10_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2020.3047732"},{"key":"e_1_2_10_29_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.08.017"},{"key":"e_1_2_10_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2018.2887023"},{"key":"e_1_2_10_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2019.01.075"},{"key":"e_1_2_10_32_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2017.12.051"},{"key":"e_1_2_10_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/tsg.2017.2686012"},{"key":"e_1_2_10_34_2","doi-asserted-by":"publisher","DOI":"10.3390\/en10101525"},{"key":"e_1_2_10_35_2","doi-asserted-by":"publisher","DOI":"10.3390\/en11020358"},{"key":"e_1_2_10_36_2","doi-asserted-by":"publisher","DOI":"10.3390\/electronics7100222"},{"key":"e_1_2_10_37_2","first-page":"3104","article-title":"Sequence to sequence learning with neural networks","volume":"1","author":"Sutskever I.","year":"2014","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_10_38_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_2_10_39_2","doi-asserted-by":"publisher","DOI":"10.3390\/s18072220"},{"key":"e_1_2_10_40_2","first-page":"843","article-title":"Unsupervised learning of video representations using LSTMs","author":"Srivastava N.","year":"2015","journal-title":"International conference on machine learning"},{"key":"e_1_2_10_41_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2005.06.042"},{"key":"e_1_2_10_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2016.2582924"},{"key":"e_1_2_10_43_2","unstructured":"Georges HebrailA. B. Individual household electric power consumption Data Set 2012 https:\/\/archive.ics.uci.edu\/ml\/datasets\/individual+household+electric+power+consumption."},{"key":"e_1_2_10_44_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.segan.2016.02.005"},{"key":"e_1_2_10_45_2","doi-asserted-by":"publisher","DOI":"10.3390\/en12040739"},{"key":"e_1_2_10_46_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2019.05.230"},{"key":"e_1_2_10_47_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2963045"},{"key":"e_1_2_10_48_2","doi-asserted-by":"publisher","DOI":"10.1002\/er.6093"},{"key":"e_1_2_10_49_2","doi-asserted-by":"publisher","DOI":"10.1109\/tii.2021.3116377"},{"key":"e_1_2_10_50_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2020.102370"}],"container-title":["Complexity"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2022\/2993184","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1155\/2022\/2993184","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2022\/2993184","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T21:17:50Z","timestamp":1769116670000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2022\/2993184"}},"subtitle":[],"editor":[{"given":"Xiaoqing","family":"Bai","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2022,1]]},"references-count":50,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["10.1155\/2022\/2993184"],"URL":"https:\/\/doi.org\/10.1155\/2022\/2993184","archive":["Portico"],"relation":{},"ISSN":["1076-2787","1099-0526"],"issn-type":[{"value":"1076-2787","type":"print"},{"value":"1099-0526","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1]]},"assertion":[{"value":"2022-06-10","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-07-19","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-08-26","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"2993184"}}