{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T11:42:57Z","timestamp":1775648577505,"version":"3.50.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"17","license":[{"start":{"date-parts":[[2023,4,4]],"date-time":"2023-04-04T00:00:00Z","timestamp":1680566400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,4,4]],"date-time":"2023-04-04T00:00:00Z","timestamp":1680566400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1007\/s10489-023-04589-2","type":"journal-article","created":{"date-parts":[[2023,4,4]],"date-time":"2023-04-04T11:41:54Z","timestamp":1680608514000},"page":"20256-20271","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A novel gated dual convolutional neural network model with autoregressive method and attention mechanism for probabilistic load forecasting"],"prefix":"10.1007","volume":"53","author":[{"given":"Yilei","family":"Qiu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shunzhen","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6405-584X","authenticated-orcid":false,"given":"Shuai","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiyuan","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,4,4]]},"reference":[{"issue":"4","key":"4589_CR1","doi-asserted-by":"publisher","first-page":"3668","DOI":"10.1109\/TPWRS.2021.3050837","volume":"36","author":"V Alvarez","year":"2021","unstructured":"Alvarez V, Mazuelas S, Lozano JA (2021) Probabilistic load forecasting based on adaptive online learning. IEEE Trans Power Syst 36(4):3668\u20133680","journal-title":"IEEE Trans Power Syst"},{"key":"4589_CR2","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1016\/j.apenergy.2019.01.022","volume":"237","author":"L Xu","year":"2019","unstructured":"Xu L, Wang SW, Tang R (2019) Probabilistic load forecasting for buildings considering weather forecasting uncertainty and uncertain peak load. Appl Energy 237:180\u2013195","journal-title":"Appl Energy"},{"issue":"4","key":"4589_CR3","doi-asserted-by":"publisher","first-page":"2485","DOI":"10.1007\/s10489-020-01932-9","volume":"51","author":"ZY Sheng","year":"2021","unstructured":"Sheng ZY, Wang HW, Chen G, Zhou B, Sun J (2021) Convolutional residual network to short-term load forecasting. Appl Intell 51(4):2485\u20132499","journal-title":"Appl Intell"},{"key":"4589_CR4","doi-asserted-by":"publisher","first-page":"886","DOI":"10.1016\/j.energy.2019.03.010","volume":"174","author":"M Barman","year":"2019","unstructured":"Barman M, Choudhury NBD (2019) Season specific approach for short-term load forecasting based on hybrid FA-SVM and similarity concept. Energy 174:886\u2013896","journal-title":"Energy"},{"key":"4589_CR5","doi-asserted-by":"publisher","first-page":"2449","DOI":"10.1109\/TMM.2021.3081873","volume":"24","author":"H Liu","year":"2022","unstructured":"Liu H, Fang S, Zhang ZL, Li DTC, Lin K, Wang JZ (2022) MFDNet: collaborative poses perception and matrix fisher distribution for head pose estimation. IEEE Trans Multimed 24:2449\u20132460","journal-title":"IEEE Trans Multimed"},{"key":"4589_CR6","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1016\/j.neucom.2020.12.090","volume":"436","author":"TT Liu","year":"2021","unstructured":"Liu TT, Wang JX, Yang B, Wang X (2021) NGDNet: nonuniform Gaussian-label distribution learning for infrared head pose estimation and on-task behavior understanding in the classroom. Neurocomputing 436:210\u2013220","journal-title":"Neurocomputing"},{"key":"4589_CR7","doi-asserted-by":"publisher","first-page":"120480","DOI":"10.1016\/j.energy.2021.120480","volume":"227","author":"M Imani","year":"2021","unstructured":"Imani M (2021) Electrical load-temperature CNN for residential load forecasting. Energy 227:120480","journal-title":"Energy"},{"issue":"12","key":"4589_CR8","doi-asserted-by":"publisher","first-page":"8243","DOI":"10.1109\/TII.2021.3065718","volume":"17","author":"SMJ Jalali","year":"2021","unstructured":"Jalali SMJ, Ahmadian S, Khosravi A, Shafie-khah M, Nahavandi S, Catal\u00e3o JP (2021) A novel evolutionary-based deep convolutional neural network model for intelligent load forecasting. IEEE Trans Ind Inform 17(12):8243\u20138253","journal-title":"IEEE Trans Ind Inform"},{"key":"4589_CR9","doi-asserted-by":"publisher","first-page":"117902","DOI":"10.1016\/j.energy.2020.117902","volume":"203","author":"Q Huang","year":"2020","unstructured":"Huang Q, Li JH, Zhu MS (2020) An improved convolutional neural network with load range discretization for probabilistic load forecasting. Energy 203:117902","journal-title":"Energy"},{"key":"4589_CR10","doi-asserted-by":"publisher","first-page":"810","DOI":"10.1016\/j.energy.2018.07.019","volume":"160","author":"WJ Zhang","year":"2018","unstructured":"Zhang WJ, Quan H, Srinivasan D (2018) Parallel and reliable probabilistic load forecasting via quantile regression forest and quantile determination. Energy 160:810\u2013819","journal-title":"Energy"},{"issue":"4","key":"4589_CR11","doi-asserted-by":"publisher","first-page":"3648","DOI":"10.1109\/TSG.2021.3066567","volume":"12","author":"HL Wen","year":"2021","unstructured":"Wen HL, Gu J, Ma JH, Yuan L, Jin ZJ (2021) Probabilistic load forecasting via neural basis expansion model based prediction intervals. IEEE Trans Smart Grid 12(4):3648\u20133660","journal-title":"IEEE Trans Smart Grid"},{"key":"4589_CR12","doi-asserted-by":"publisher","first-page":"118341","DOI":"10.1016\/j.apenergy.2021.118341","volume":"309","author":"A Brusaferri","year":"2022","unstructured":"Brusaferri A, Matteucci M, Spinelli S, Vitali A (2022) Probabilistic electric load forecasting through Bayesian mixture density networks. Appl Energy 309:118341","journal-title":"Appl Energy"},{"issue":"6","key":"4589_CR13","doi-asserted-by":"publisher","first-page":"5396","DOI":"10.1109\/TSG.2021.3101672","volume":"12","author":"C Wan","year":"2021","unstructured":"Wan C, Cao Z, Lee WJ, Song Y, Ju P (2021) An adaptive ensemble data driven approach for nonparametric probabilistic forecasting of electricity load. IEEE Trans Smart Grid 12(6):5396\u20135408","journal-title":"IEEE Trans Smart Grid"},{"key":"4589_CR14","doi-asserted-by":"publisher","first-page":"116249","DOI":"10.1016\/j.apenergy.2020.116249","volume":"282","author":"D Jeong","year":"2021","unstructured":"Jeong D, Park C, Ko YM (2021) Short-term electric load forecasting for buildings using logistic mixture vector autoregressive model with curve registration. Appl Energy 282:116249","journal-title":"Appl Energy"},{"key":"4589_CR15","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.apenergy.2018.10.078","volume":"235","author":"Y Wang","year":"2019","unstructured":"Wang Y, Gan DH, Sun MY, Zhang N, Lu ZX, Kang CQ (2019) Probabilistic individual load forecasting using pinball loss guided LSTM. Appl Energy 235:10\u201320","journal-title":"Appl Energy"},{"issue":"3","key":"4589_CR16","doi-asserted-by":"publisher","first-page":"628","DOI":"10.1109\/TETCI.2021.3064028","volume":"6","author":"P Arora","year":"2022","unstructured":"Arora P, Khosravi A, Panigrahi BK, Suganthan PN (2022) Remodelling state-space prediction with deep neural networks for probabilistic load forecasting. IEEE Trans Emerg Topics Comput Intell 6(3):628\u2013637","journal-title":"IEEE Trans Emerg Topics Comput Intell"},{"key":"4589_CR17","doi-asserted-by":"crossref","unstructured":"Lai GK, Chang WC, Yang YM, Liu HX (2018) Modeling long-and short-term temporal patterns with deep neural networks. In the 41st international ACM SIGIR conference on Research & Development in information retrieval, July 8-12, MI, USA, pp 95\u2013104","DOI":"10.1145\/3209978.3210006"},{"issue":"4","key":"4589_CR18","doi-asserted-by":"publisher","first-page":"4425","DOI":"10.1109\/TSG.2018.2859749","volume":"10","author":"WJ Zhang","year":"2018","unstructured":"Zhang WJ, Quan H, Srinivasan D (2018) An improved quantile regression neural network for probabilistic load forecasting. IEEE Trans Smart Grid 10(4):4425\u20134434","journal-title":"IEEE Trans Smart Grid"},{"key":"4589_CR19","doi-asserted-by":"publisher","first-page":"116324","DOI":"10.1016\/j.energy.2019.116324","volume":"189","author":"YD Yang","year":"2019","unstructured":"Yang YD, Hong WJ, Li SF (2019) Deep ensemble learning based probabilistic load forecasting in smart grids. Energy 189:116324","journal-title":"Energy"},{"issue":"6","key":"4589_CR20","first-page":"983","volume":"7","author":"N Meinshausen","year":"2006","unstructured":"Meinshausen N, Ridgeway G (2006) Quantile regression forests. J Mach Learn Res 7(6):983\u2013999","journal-title":"J Mach Learn Res"},{"key":"4589_CR21","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1016\/j.apenergy.2018.10.061","volume":"233","author":"YY He","year":"2019","unstructured":"He YY, Qin Y, Wang S, Wang X, Wang C (2019) Electricity consumption probability density forecasting method based on LASSO-quantile regression neural network. Appl Energy 233:565\u2013575","journal-title":"Appl Energy"},{"key":"4589_CR22","doi-asserted-by":"publisher","first-page":"120682","DOI":"10.1016\/j.energy.2021.120682","volume":"229","author":"HX Zang","year":"2021","unstructured":"Zang HX, Xu RQ, Cheng LL, Ding T, Liu L, Wei ZN, Sun GQ (2021) Residential load forecasting based on LSTM fusing self-attention mechanism with pooling. Energy 229:120682","journal-title":"Energy"},{"issue":"2","key":"4589_CR23","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1007\/s40565-018-0380-x","volume":"6","author":"DH Gan","year":"2018","unstructured":"Gan DH, Wang Y, Yang S, Kang CQ (2018) Embedding based quantile regression neural network for probabilistic load forecasting. J Mod Power Syst Clean Energy 6(2):244\u2013254","journal-title":"J Mod Power Syst Clean Energy"},{"issue":"3","key":"4589_CR24","first-page":"1680","volume":"9","author":"J Xie","year":"2018","unstructured":"Xie J, Hong T (2018) Temperature scenario generation for probabilistic load forecasting. IEEE Trans Smart Grid 9(3):1680\u20131687","journal-title":"IEEE Trans Smart Grid"},{"key":"4589_CR25","doi-asserted-by":"publisher","first-page":"102443","DOI":"10.1016\/j.sysarc.2022.102443","volume":"125","author":"YW Lou","year":"2022","unstructured":"Lou YW, Huang Y, Xing XL, Cao YZ, Wang HP (2022) MTS-LSTDM: multi-time-scale long short-term double memory for power load forecasting. J Syst Archit 125:102443","journal-title":"J Syst Archit"},{"key":"4589_CR26","doi-asserted-by":"publisher","unstructured":"Liu H, Liu T, Chen Y, Zhang Z, Li YF (2022) EHPE: skeleton cues-based gaussian coordinate encoding for efficient human pose estimation. IEEE Trans Multimed:1\u201312. https:\/\/doi.org\/10.1109\/TMM.2022.3197364","DOI":"10.1109\/TMM.2022.3197364"},{"key":"4589_CR27","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1016\/j.neucom.2020.09.068","volume":"433","author":"H Liu","year":"2021","unstructured":"Liu H, Nie HW, Zhang ZL, Li YF (2021) Anisotropic angle distribution learning for head pose estimation and attention understanding in human-computer interaction. Neurocomputing 433:310\u2013322","journal-title":"Neurocomputing"},{"issue":"8","key":"4589_CR28","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780","journal-title":"Neural Comput"},{"key":"4589_CR29","doi-asserted-by":"publisher","first-page":"101182","DOI":"10.1016\/j.csl.2020.101182","volume":"68","author":"JF Deng","year":"2021","unstructured":"Deng JF, Cheng LL, Wang ZW (2021) Attention-based BiLSTM fused CNN with gating mechanism model for Chinese long text classification. Comput Speech Lang 68:101182","journal-title":"Comput Speech Lang"},{"issue":"5","key":"4589_CR30","doi-asserted-by":"publisher","first-page":"5867","DOI":"10.1007\/s10489-021-02724-5","volume":"52","author":"YQ Peng","year":"2022","unstructured":"Peng YQ, Xiao TF, Yuan HT (2022) Cooperative gating network based on a single BERT encoder for aspect term sentiment analysis. Appl Intell 52(5):5867\u20135879","journal-title":"Appl Intell"},{"key":"4589_CR31","doi-asserted-by":"crossref","unstructured":"Wu ZH, Pan SR, Long GD, Jiang J, Zhang CQ (2019) Graph wavenet for deep spatial-temporal graph modeling. In the 28th International Joint Conference on Artificial Intelligence, august 10-16, Macao, China, pp 1907\u20131913","DOI":"10.24963\/ijcai.2019\/264"},{"key":"4589_CR32","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.neucom.2022.10.033","volume":"516","author":"D Ienco","year":"2023","unstructured":"Ienco D, Interdonato R (2023) Deep semi-supervised clustering for multi-variate time-series. Neurocomputing 516:36\u201347","journal-title":"Neurocomputing"},{"issue":"4","key":"4589_CR33","doi-asserted-by":"publisher","first-page":"3646","DOI":"10.1109\/TSG.2020.2972513","volume":"11","author":"M Afrasiabi","year":"2020","unstructured":"Afrasiabi M, Mohammadi M, Rastegar M, Stankovic L, Afrasiabi S, Khazaei M (2020) Deep-based conditional probability density function forecasting of residential loads. IEEE Trans Smart Grid 11(4):3646\u20133657","journal-title":"IEEE Trans Smart Grid"},{"key":"4589_CR34","doi-asserted-by":"publisher","first-page":"107802","DOI":"10.1016\/j.epsr.2022.107802","volume":"206","author":"RH Liu","year":"2022","unstructured":"Liu RH, Chen T, Sun GP, Muyeen SM, Lin SF, Mi Y (2022) Short-term probabilistic building load forecasting based on feature integrated artificial intelligent approach. Electr Power Syst Res 206:107802","journal-title":"Electr Power Syst Res"},{"issue":"7","key":"4589_CR35","doi-asserted-by":"publisher","first-page":"4361","DOI":"10.1109\/TII.2021.3128240","volume":"18","author":"H Liu","year":"2021","unstructured":"Liu H, Zheng C, Li DTCL, Shen XX, Lin K, Wang JZ et al (2021) EDMF: efficient deep matrix factorization with review feature learning for industrial recommender system. IEEE Trans Ind Inform 18(7):4361\u20134371","journal-title":"IEEE Trans Ind Inform"},{"issue":"8","key":"4589_CR36","doi-asserted-by":"publisher","first-page":"3961","DOI":"10.1109\/TNNLS.2021.3055147","volume":"33","author":"ZF Li","year":"2022","unstructured":"Li ZF, Liu H, Zhang ZL, Liu TT, Xiong NN (2022) Learning knowledge graph embedding with heterogeneous relation attention networks. IEEE Trans Neural Netw Learn Syst 33(8):3961\u20133973","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"4589_CR37","doi-asserted-by":"publisher","unstructured":"Liu JS, Kang Y, Li H, Wang HN, Yang XK (2022) STGHTN: spatial-temporal gated hybrid transformer network for traffic flow forecasting. Appl Intell. https:\/\/doi.org\/10.1007\/s10489-022-04122-x","DOI":"10.1007\/s10489-022-04122-x"},{"key":"4589_CR38","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1016\/j.neucom.2019.01.078","volume":"337","author":"G Liu","year":"2019","unstructured":"Liu G, Guo JB (2019) Bidirectional LSTM with attention mechanism and convolutional layer for text classification. Neurocomputing 337:325\u2013338","journal-title":"Neurocomputing"},{"key":"4589_CR39","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.neucom.2021.03.091","volume":"452","author":"ZY Niu","year":"2021","unstructured":"Niu ZY, Zhong GQ, Yu H (2021) A review on the attention mechanism of deep learning. Neurocomputing 452:48\u201362","journal-title":"Neurocomputing"},{"key":"4589_CR40","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1016\/j.ijepes.2019.02.022","volume":"109","author":"SX Wang","year":"2019","unstructured":"Wang SX, Wang X, Wang SM, Wang D (2019) Bi-directional long short-term memory method based on attention mechanism and rolling update for short-term load forecasting. Int J Electr Power Energy Syst 109:470\u2013479","journal-title":"Int J Electr Power Energy Syst"},{"key":"4589_CR41","doi-asserted-by":"publisher","unstructured":"Huang Y, Huang Z, Yu JH, Dai XH, Li YY (2022) Short-term load forecasting based on IPSO-DBiLSTM network with variational mode decomposition and attention mechanism. Appl Intell. https:\/\/doi.org\/10.1007\/s10489-022-04174-z","DOI":"10.1007\/s10489-022-04174-z"},{"key":"4589_CR42","doi-asserted-by":"publisher","first-page":"107818","DOI":"10.1016\/j.ijepes.2021.107818","volume":"137","author":"J Lin","year":"2022","unstructured":"Lin J, Ma J, Zhu JG, Cui Y (2022) Short-term load forecasting based on LSTM networks considering attention mechanism. Int J Electr Power Energy Syst 137:107818","journal-title":"Int J Electr Power Energy Syst"},{"issue":"4","key":"4589_CR43","doi-asserted-by":"publisher","first-page":"2205","DOI":"10.1109\/TSTE.2021.3086851","volume":"12","author":"H Zhang","year":"2021","unstructured":"Zhang H, Yan J, Liu YQ, Gao YQ, Han S, Li L (2021) Multi-source and temporal attention network for probabilistic wind power prediction. IEEE Trans Sustain Energy 12(4):2205\u20132218","journal-title":"IEEE Trans Sustain Energy"},{"key":"4589_CR44","doi-asserted-by":"crossref","unstructured":"Huang ST, Wang DL, Wu XH, Tang A (2019) Dsanet: dual self-attention network for multivariate time series forecasting. In the 28th ACM international conference on information and knowledge management, November 3\u20137, Beijing, China, pp 2129\u20132132","DOI":"10.1145\/3357384.3358132"},{"key":"4589_CR45","doi-asserted-by":"publisher","first-page":"121492","DOI":"10.1016\/j.energy.2021.121492","volume":"236","author":"WY Zhang","year":"2021","unstructured":"Zhang WY, Chen Q, Yan JY, Zhang S, Xu JY (2021) A novel asynchronous deep reinforcement learning model with adaptive early forecasting method and reward incentive mechanism for short-term load forecasting. Energy 236:121492","journal-title":"Energy"},{"key":"4589_CR46","doi-asserted-by":"publisher","first-page":"32436","DOI":"10.1109\/ACCESS.2021.3060654","volume":"9","author":"SH Rafi","year":"2021","unstructured":"Rafi SH, Deeba SR, Hossain E (2021) A short-term load forecasting method using integrated CNN and LSTM network. IEEE Access 9:32436\u201332448","journal-title":"IEEE Access"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04589-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-023-04589-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04589-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T11:37:07Z","timestamp":1694777827000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-023-04589-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,4]]},"references-count":46,"journal-issue":{"issue":"17","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["4589"],"URL":"https:\/\/doi.org\/10.1007\/s10489-023-04589-2","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,4]]},"assertion":[{"value":"23 March 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 April 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}