{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T16:49:12Z","timestamp":1780332552468,"version":"3.54.1"},"reference-count":23,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2019,1,9]],"date-time":"2019-01-09T00:00:00Z","timestamp":1546992000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"Scientific and Technological Planning Project of Jilin Province","award":["2018C0361"],"award-info":[{"award-number":["2018C0361"]}]},{"name":"Scientific and Technological Planning Project of Jilin Province","award":["20180101057JC"],"award-info":[{"award-number":["20180101057JC"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Evol. Intel."],"published-print":{"date-parts":[[2019,9]]},"DOI":"10.1007\/s12065-018-00196-0","type":"journal-article","created":{"date-parts":[[2019,1,9]],"date-time":"2019-01-09T06:44:45Z","timestamp":1547016285000},"page":"385-394","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":93,"title":["Short-term electrical load forecasting method based on stacked auto-encoding and GRU neural network"],"prefix":"10.1007","volume":"12","author":[{"given":"Kang","family":"Ke","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sun","family":"Hongbin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhang","family":"Chengkang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carl","family":"Brown","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,1,9]]},"reference":[{"key":"196_CR1","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.enbuild.2014.08.004","volume":"84","author":"JG Jetcheva","year":"2014","unstructured":"Jetcheva JG, Majidpour M, Chen WP (2014) Neural network model ensembles for building-level electricity load forecasts. Energy Build 84:214\u2013223","journal-title":"Energy Build"},{"issue":"1","key":"196_CR2","first-page":"12","volume":"180","author":"Y Mulyadi","year":"2017","unstructured":"Mulyadi Y, Abdullah AG, Rohmah KA (2017) Optimize short term load forcasting anomalous based feed forward backpropagation. IOP Conf Ser Mater Sci Eng 180(1):12\u201376","journal-title":"IOP Conf Ser Mater Sci Eng"},{"issue":"2","key":"196_CR3","doi-asserted-by":"publisher","first-page":"285","DOI":"10.29194\/NJES21020285","volume":"21","author":"HA Akkar","year":"2018","unstructured":"Akkar HA, Ali WH (2018) Estimation load forecasting based on the intelligent systems. Al-Nahrain J Eng Sci 21(2):285\u2013291","journal-title":"Al-Nahrain J Eng Sci"},{"issue":"1","key":"196_CR4","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1109\/TSG.2013.2274373","volume":"5","author":"T Hong","year":"2014","unstructured":"Hong T, Wilson J, Xie J (2014) Long term probabilistic load forecasting and normalization with hourly information. IEEE Trans Smart Grid 5(1):456\u2013462","journal-title":"IEEE Trans Smart Grid"},{"issue":"14","key":"196_CR5","first-page":"3678","volume":"35","author":"F Chen","year":"2015","unstructured":"Chen F, Xu J, Wang C (2015) Research on building cooling and heating load prediction model on user\u2019s side in energy internet system. Proc CSEE 35(14):3678\u20133684","journal-title":"Proc CSEE"},{"issue":"7","key":"196_CR6","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","volume":"18","author":"GE Hinton","year":"2006","unstructured":"Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527\u20131554","journal-title":"Neural Comput"},{"key":"196_CR7","doi-asserted-by":"crossref","unstructured":"Brown RH, Vitullo SR, Corliss GF et al (2015) Detrending daily natural gas consumption series to improve short-term forecasts. In: Power and energy society general meeting, IEEE. IEEE, pp 1\u20135","DOI":"10.1109\/PESGM.2015.7286138"},{"key":"196_CR8","doi-asserted-by":"crossref","unstructured":"Brown RH, Vitullo SR, Corliss GF, Adya M, Kaefer PE, Povinelli RJ (2015) Detrending daily natural gas consumption series to improve short-term forecasts. In: Power and energy society general meeting, 2015 IEEE (pp\u00a01\u20135). IEEE","DOI":"10.1109\/PESGM.2015.7286138"},{"issue":"3","key":"196_CR9","doi-asserted-by":"publisher","first-page":"914","DOI":"10.1016\/j.ijforecast.2015.11.011","volume":"32","author":"T Hong","year":"2016","unstructured":"Hong T, Fan S (2016) Probabilistic electric load forecasting: A tutorial review. Int J Forecast 32(3):914\u2013938","journal-title":"Int J Forecast"},{"issue":"1","key":"196_CR10","first-page":"9","volume":"32","author":"C Peng","year":"2012","unstructured":"Peng C, Liu G, Sun H (2012) Wind speed forecasting based on wavelet decomposition and differential evolution-support vector machine for wind farms. Electric Power Autom Equip 32(1):9\u201313","journal-title":"Electric Power Autom Equip"},{"issue":"8","key":"196_CR11","first-page":"1829","volume":"29","author":"H Sun","year":"2017","unstructured":"Sun H, Xie B, Tian Y, Li Z (2017) Forecasting of short-term power load of SecRPSO-SVM based on data-driven. J Syst Simul 29(8):1829\u20131836","journal-title":"J Syst Simul"},{"issue":"2","key":"196_CR12","first-page":"121","volume":"49","author":"Y Zhang","year":"2017","unstructured":"Zhang Y, Hu B, Li S, Luo Y (2017) The short-term power load forecasting model of combing ILMD and ESN based on similar days searching. J Zhengzhou Univ (Nat Sci Edition) 49(2):121\u2013127","journal-title":"J Zhengzhou Univ (Nat Sci Edition)"},{"key":"196_CR13","doi-asserted-by":"publisher","first-page":"524","DOI":"10.1016\/j.energy.2015.01.063","volume":"82","author":"L Xiao","year":"2015","unstructured":"Xiao L, Wang J, Hou R, Wu J (2015) A combined model based on data pre-analysis and weight coefficients optimization for electrical load forecasting. Energy 82:524\u2013549","journal-title":"Energy"},{"issue":"2","key":"196_CR14","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1109\/TNNLS.2013.2276053","volume":"25","author":"H Quan","year":"2014","unstructured":"Quan H, Srinivasan D, Khosravi A (2014) Short-term load and wind power forecasting using neural network-based prediction intervals. IEEE Trans Neural Netw Learn Syst 25(2):303\u2013315","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"3","key":"196_CR15","first-page":"698","volume":"42","author":"J Shi","year":"2018","unstructured":"Shi J, Tan T, Guo J, Liu Y, Zhang J (2018) Multi-task learning based on deep architecture for various types of load forecasting in regional energy system integration. Power Syst Technol 42(3):698\u2013707","journal-title":"Power Syst Technol"},{"issue":"8","key":"196_CR16","first-page":"25","volume":"20","author":"Y Xu","year":"2017","unstructured":"Xu Y, Fang L, Zhao D, Wang K (2017) Electricity consumption prediction based on LSTM neural networks. Power Systems Big Data 20(8):25\u201329","journal-title":"Power Systems Big Data"},{"key":"196_CR17","doi-asserted-by":"crossref","unstructured":"Zhang P, Ma X, Zhang W, Lin S, Chen H, Yirun AL, Xiao G (2015) Multimodal fusion for sensor data using stacked autoencoders. In: 2015 IEEE tenth international conference on intelligent sensors, sensor networks and information processing (ISSNIP), pp 1\u20132. IEEE","DOI":"10.1109\/ISSNIP.2015.7106972"},{"issue":"5","key":"196_CR18","first-page":"122","volume":"36","author":"X Shi","year":"2016","unstructured":"Shi X, Zhu Y, Ning X, Wang LW, Sun G, Chen GQ (2016) Transformer fault diagnosis based on deep auto-encoder network. Electric Power Autom Equip 36(5):122\u2013126","journal-title":"Electric Power Autom Equip"},{"issue":"12","key":"196_CR19","first-page":"109","volume":"38","author":"Y Li","year":"2017","unstructured":"Li Y, Huang J, Wang H, Zhong N (2017) Study of emotion recognition based on fusion multi-modal bio-signal with SAE and LSTM recurrent neural network. J Commun 38(12):109\u2013120","journal-title":"J Commun"},{"issue":"5\u20136","key":"196_CR20","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1016\/j.neunet.2005.06.042","volume":"18","author":"A Graves","year":"2005","unstructured":"Graves A, Schmidhuber J (2005) Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw 18(5\u20136):602\u2013610","journal-title":"Neural Netw"},{"key":"196_CR21","doi-asserted-by":"crossref","unstructured":"Gers FA, Schmidhuber J, Cummins F (1999) Learning to forget: continual prediction with LSTM. In: 9th international conference on artificial neural networks: ICANN \u201999, pp 850\u2013855, ICANN","DOI":"10.1049\/cp:19991218"},{"issue":"10","key":"196_CR22","doi-asserted-by":"publisher","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","volume":"28","author":"K Greff","year":"2017","unstructured":"Greff K, Srivastava RK, Koutn\u00edk J, Steunebrink BR, Schmidhuber J (2017) LSTM: a search space odyssey. IEEE Trans Neural Netw Learn Syst 28(10):2222\u20132232","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"5","key":"196_CR23","first-page":"36","volume":"38","author":"Z Niu","year":"2018","unstructured":"Niu Z, Yu Z, Li B, Tang W (2018) Short-term wind power forecasting model based on deep gated recurrent unit neural network. Electric Power Autom Equip 38(5):36\u201342","journal-title":"Electric Power Autom Equip"}],"container-title":["Evolutionary Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12065-018-00196-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s12065-018-00196-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12065-018-00196-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,1,9]],"date-time":"2020-01-09T00:20:32Z","timestamp":1578529232000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s12065-018-00196-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,9]]},"references-count":23,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2019,9]]}},"alternative-id":["196"],"URL":"https:\/\/doi.org\/10.1007\/s12065-018-00196-0","relation":{},"ISSN":["1864-5909","1864-5917"],"issn-type":[{"value":"1864-5909","type":"print"},{"value":"1864-5917","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,1,9]]},"assertion":[{"value":"10 November 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 December 2018","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 December 2018","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 January 2019","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}