{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T10:35:27Z","timestamp":1775730927243,"version":"3.50.1"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T00:00:00Z","timestamp":1694390400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T00:00:00Z","timestamp":1694390400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62102241"],"award-info":[{"award-number":["62102241"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018625","name":"Science and Technology Innovation Plan Of Shanghai Science and Technology Commission","doi-asserted-by":"publisher","award":["No.23ZR1425400"],"award-info":[{"award-number":["No.23ZR1425400"]}],"id":[{"id":"10.13039\/501100018625","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1007\/s13042-023-01963-x","type":"journal-article","created":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T09:02:00Z","timestamp":1694422920000},"page":"1201-1212","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A k-nearest neighbor attentive deep autoregressive network for electricity consumption prediction"],"prefix":"10.1007","volume":"15","author":[{"given":"Xihe","family":"Qiu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yajun","family":"Ru","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyu","family":"Tan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jue","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yun","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,11]]},"reference":[{"issue":"2","key":"1963_CR1","doi-asserted-by":"publisher","first-page":"1111","DOI":"10.1109\/TII.2020.2980160","volume":"17","author":"A Paudel","year":"2020","unstructured":"Paudel A, Khorasany M, Gooi HB (2020) Decentralized local energy trading in microgrids with voltage management. IEEE Trans Ind Inform 17(2):1111\u20131121","journal-title":"IEEE Trans Ind Inform"},{"key":"1963_CR2","doi-asserted-by":"publisher","first-page":"3014","DOI":"10.1016\/j.egypro.2015.07.616","volume":"75","author":"L Friedrich","year":"2015","unstructured":"Friedrich L, Afshari A (2015) Short-term forecasting of the Abu Dhabi electricity load using multiple weather variables. Energy Proc 75:3014\u20133026","journal-title":"Energy Proc"},{"issue":"2","key":"1963_CR3","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1007\/s40565-017-0351-7","volume":"6","author":"J Luo","year":"2018","unstructured":"Luo J, Hong T, Yue M (2018) Real-time anomaly detection for very short-term load forecasting. J Mod Power Syst Clean Energy 6(2):235\u2013243","journal-title":"J Mod Power Syst Clean Energy"},{"key":"1963_CR4","doi-asserted-by":"crossref","unstructured":"Zor K, Timur O, Teke A (2017) A state-of-the-art review of artificial intelligence techniques for short-term electric load forecasting. In: 2017 6th International Youth Conference on Energy (IYCE), pp 1\u20137. IEEE","DOI":"10.1109\/IYCE.2017.8003734"},{"key":"1963_CR5","doi-asserted-by":"crossref","unstructured":"Desportes L, Andry P, Fijalkow I, David J (2019) Short-term temperature forecasting on a several hours horizon. In: Artificial neural networks and machine learning\u2013ICANN 2019: Text and Time Series: 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17\u201319, 2019, Proceedings, Part IV 28, pp 525\u2013536. Springer","DOI":"10.1007\/978-3-030-30490-4_42"},{"key":"1963_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.105739","volume":"84","author":"Q Zhu","year":"2019","unstructured":"Zhu Q, Zhang F, Liu S, Wu Y, Wang L (2019) A hybrid vmd-bigru model for rubber futures time series forecasting. Appl Soft Comput 84:105739","journal-title":"Appl Soft Comput"},{"key":"1963_CR7","doi-asserted-by":"crossref","unstructured":"Juberias G, Yunta R, Moreno JG, Mendivil C (1999) A new arima model for hourly load forecasting. In: 1999 IEEE Transmission and Distribution Conference (Cat. No. 99CH36333), vol. 1, pp 314\u2013319. IEEE","DOI":"10.1109\/TDC.1999.755371"},{"issue":"6","key":"1963_CR8","doi-asserted-by":"publisher","first-page":"1333","DOI":"10.1080\/02664763.2013.785496","volume":"40","author":"S Bercu","year":"2013","unstructured":"Bercu S, Pro\u00efa F (2013) A sarimax coupled modelling applied to individual load curves intraday forecasting. J Appl Stat 40(6):1333\u20131348","journal-title":"J Appl Stat"},{"issue":"1","key":"1963_CR9","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1109\/TPWRS.2011.2161780","volume":"27","author":"JW Taylor","year":"2011","unstructured":"Taylor JW (2011) Short-term load forecasting with exponentially weighted methods. IEEE Trans Power Syst 27(1):458\u2013464","journal-title":"IEEE Trans Power Syst"},{"key":"1963_CR10","doi-asserted-by":"publisher","first-page":"8439719","DOI":"10.1155\/2020\/8439719","volume":"15","author":"M Ibrahim","year":"2020","unstructured":"Ibrahim M, Alsheikh A, Al-Hindawi Q, Al-Dahidi S, ElMoaqet H (2020) Short-time wind speed forecast using artificial learning-based algorithms. Comput Intell Neurosci 15:8439719. https:\/\/doi.org\/10.1155\/2020\/8439719","journal-title":"Comput Intell Neurosci"},{"issue":"3","key":"1963_CR11","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1007\/s12065-018-00196-0","volume":"12","author":"K Ke","year":"2019","unstructured":"Ke K, Hongbin S, Chengkang Z, Brown C (2019) Short-term electrical load forecasting method based on stacked auto-encoding and gru neural network. Evol Intel 12(3):385\u2013394","journal-title":"Evol Intel"},{"issue":"3","key":"1963_CR12","doi-asserted-by":"publisher","first-page":"1181","DOI":"10.1016\/j.ijforecast.2019.07.001","volume":"36","author":"D Salinas","year":"2020","unstructured":"Salinas D, Flunkert V, Gasthaus J, Januschowski T (2020) Deepar: probabilistic forecasting with autoregressive recurrent networks. Int J Forecast 36(3):1181\u20131191","journal-title":"Int J Forecast"},{"issue":"1","key":"1963_CR13","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1080\/00031305.2017.1380080","volume":"72","author":"SJ Taylor","year":"2018","unstructured":"Taylor SJ, Letham B (2018) Forecasting at scale. Am Stat 72(1):37\u201345","journal-title":"Am Stat"},{"issue":"7","key":"1963_CR14","doi-asserted-by":"publisher","first-page":"1235","DOI":"10.1162\/neco_a_01199","volume":"31","author":"Y Yu","year":"2019","unstructured":"Yu Y, Si X, Hu C, Zhang J (2019) A review of recurrent neural networks: Lstm cells and network architectures. Neural Comput 31(7):1235\u20131270","journal-title":"Neural Comput"},{"issue":"7","key":"1963_CR15","first-page":"75","volume":"13","author":"G Shilpa","year":"2017","unstructured":"Shilpa G, Sheshadri G (2017) Short-term load forecasting using arima model for Karnataka state electrical load. Int J Eng Res Dev 13(7):75\u201379","journal-title":"Int J Eng Res Dev"},{"key":"1963_CR16","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.ijepes.2016.03.012","volume":"82","author":"S Barak","year":"2016","unstructured":"Barak S, Sadegh SS (2016) Forecasting energy consumption using ensemble arima-anfis hybrid algorithm. Int J Electric Power Energy Syst 82:92\u2013104","journal-title":"Int J Electric Power Energy Syst"},{"issue":"9","key":"1963_CR17","doi-asserted-by":"publisher","first-page":"1413","DOI":"10.1016\/j.energy.2009.06.034","volume":"34","author":"V Bianco","year":"2009","unstructured":"Bianco V, Manca O, Nardini S (2009) Electricity consumption forecasting in Italy using linear regression models. Energy 34(9):1413\u20131421","journal-title":"Energy"},{"issue":"4","key":"1963_CR18","doi-asserted-by":"publisher","first-page":"4356","DOI":"10.1109\/TPWRS.2013.2269803","volume":"28","author":"E Ceperic","year":"2013","unstructured":"Ceperic E, Ceperic V, Baric A (2013) A strategy for short-term load forecasting by support vector regression machines. IEEE Trans Power Syst 28(4):4356\u20134364","journal-title":"IEEE Trans Power Syst"},{"issue":"8","key":"1963_CR19","doi-asserted-by":"publisher","first-page":"3002","DOI":"10.1007\/s10489-019-01426-3","volume":"49","author":"W Xu","year":"2019","unstructured":"Xu W, Peng H, Zeng X, Zhou F, Tian X, Peng X (2019) A hybrid modelling method for time series forecasting based on a linear regression model and deep learning. Appl Intell 49(8):3002\u20133015","journal-title":"Appl Intell"},{"key":"1963_CR20","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.rser.2014.01.069","volume":"33","author":"AS Ahmad","year":"2014","unstructured":"Ahmad AS, Hassan MY, Abdullah MP, Rahman HA, Hussin F, Abdullah H, Saidur R (2014) A review on applications of ann and svm for building electrical energy consumption forecasting. Renew Sustain Energy Rev 33:102\u2013109","journal-title":"Renew Sustain Energy Rev"},{"issue":"2","key":"1963_CR21","doi-asserted-by":"publisher","first-page":"1630","DOI":"10.1007\/s10489-021-02473-5","volume":"52","author":"Y Yang","year":"2022","unstructured":"Yang Y, Tao Z, Qian C, Gao Y, Zhou H, Ding Z, Wu J (2022) A hybrid robust system considering outliers for electric load series forecasting. Appl Intell 52(2):1630\u20131652","journal-title":"Appl Intell"},{"issue":"3","key":"1963_CR22","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1093\/ijlct\/1.3.201","volume":"1","author":"SA Kalogirou","year":"2006","unstructured":"Kalogirou SA (2006) Artificial neural networks in energy applications in buildings. Int J Low-Carbon Technol 1(3):201\u2013216","journal-title":"Int J Low-Carbon Technol"},{"key":"1963_CR23","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1016\/j.apenergy.2017.12.051","volume":"212","author":"A Rahman","year":"2018","unstructured":"Rahman A, Srikumar V, Smith AD (2018) Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks. Appl Energy 212:372\u2013385","journal-title":"Appl Energy"},{"issue":"8","key":"1963_CR24","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":"1963_CR25","doi-asserted-by":"crossref","unstructured":"Kim N, Kim M, Choi JK (2018) Lstm based short-term electricity consumption forecast with daily load profile sequences. In: 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE), pp 136\u2013137. IEEE","DOI":"10.1109\/GCCE.2018.8574484"},{"key":"1963_CR26","doi-asserted-by":"crossref","unstructured":"Marino DL, Amarasinghe K, Manic M (2016) Building energy load forecasting using deep neural networks. In: IECON 2016-42nd Annual Conference of the IEEE Industrial Electronics Society, pp 7046\u20137051. IEEE","DOI":"10.1109\/IECON.2016.7793413"},{"key":"1963_CR27","doi-asserted-by":"publisher","first-page":"2470171","DOI":"10.1155\/2018\/2470171","volume":"8","author":"JY Choi","year":"2018","unstructured":"Choi JY (2018) Lee B (2018) Combining lstm network ensemble via adaptive weighting for improved time series forecasting. MathProbl Eng 8:2470171. https:\/\/doi.org\/10.1155\/2018\/2470171","journal-title":"MathProbl Eng"},{"key":"1963_CR28","doi-asserted-by":"publisher","first-page":"131365","DOI":"10.1109\/ACCESS.2021.3112666","volume":"9","author":"A Naz","year":"2021","unstructured":"Naz A, Javaid N, Asif M, Javed MU, Ahmed A, Gulfam SM, Shafiq M, Choi J-G (2021) Electricity consumption forecasting using gated-fcn with ensemble strategy. IEEE Access 9:131365\u2013131381","journal-title":"IEEE Access"},{"issue":"9","key":"1963_CR29","doi-asserted-by":"publisher","first-page":"1630","DOI":"10.1049\/iet-gtd.2018.6081","volume":"13","author":"W Li","year":"2019","unstructured":"Li W, Logenthiran T, Woo WL (2019) Multi-gru prediction system for electricity generation\u2019s planning and operation. IET Gener Transm Distrib 13(9):1630\u20131637","journal-title":"IET Gener Transm Distrib"},{"issue":"3","key":"1963_CR30","doi-asserted-by":"publisher","first-page":"2814","DOI":"10.1109\/TII.2022.3160696","volume":"19","author":"P Arora","year":"2022","unstructured":"Arora P, Jalali SMJ, Ahmadian S, Panigrahi B, Suganthan P, Khosravi A (2022) Probabilistic wind power forecasting using optimized deep auto-regressive recurrent neural networks. IEEE Trans Ind Inf 19(3):2814\u20132825","journal-title":"IEEE Trans Ind Inf"},{"key":"1963_CR31","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1016\/j.neucom.2020.03.011","volume":"399","author":"Y Chen","year":"2020","unstructured":"Chen Y, Kang Y, Chen Y, Wang Z (2020) Probabilistic forecasting with temporal convolutional neural network. Neurocomputing 399:491\u2013501","journal-title":"Neurocomputing"},{"key":"1963_CR32","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.neucom.2021.03.091","volume":"452","author":"Z Niu","year":"2021","unstructured":"Niu Z, Zhong G, Yu H (2021) A review on the attention mechanism of deep learning. Neurocomputing 452:48\u201362","journal-title":"Neurocomputing"},{"key":"1963_CR33","doi-asserted-by":"crossref","unstructured":"Huang P-Y, Liu F, Shiang S-R, Oh J, Dyer C (2016) Attention-based multimodal neural machine translation. In: Proceedings of the First Conference on machine translation: volume 2, Shared Task Papers, pp 639\u2013645","DOI":"10.18653\/v1\/W16-2360"},{"issue":"1","key":"1963_CR34","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1109\/TPAMI.2018.2876404","volume":"42","author":"B Zhang","year":"2018","unstructured":"Zhang B, Xiong D, Su J (2018) Neural machine translation with deep attention. IEEE Trans Pattern Anal Mach Intell 42(1):154\u2013163","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1963_CR35","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.neucom.2022.02.028","volume":"485","author":"Y Ru","year":"2022","unstructured":"Ru Y, Qiu X, Tan X, Chen B, Gao Y, Jin Y (2022) Sparse-attentive meta temporal point process for clinical decision support. Neurocomputing 485:114\u2013123","journal-title":"Neurocomputing"},{"key":"1963_CR36","doi-asserted-by":"crossref","unstructured":"Ma F, Chitta R, Zhou J, You Q, Sun T, Gao J (2017) Dipole: Diagnosis prediction in healthcare via attention-based bidirectional recurrent neural networks. In: Proceedings of the 23rd ACM SIGKDD International Conference on knowledge discovery and data mining, pp 1903\u20131911","DOI":"10.1145\/3097983.3098088"},{"key":"1963_CR37","doi-asserted-by":"crossref","unstructured":"Wang S, Hu L, Cao L, Huang X, Lian D, Liu W (2018) Attention-based transactional context embedding for next-item recommendation. In: Proceedings of the AAAI Conference on artificial intelligence, vol. 32","DOI":"10.1609\/aaai.v32i1.11851"},{"issue":"5","key":"1963_CR38","first-page":"4875","volume":"53","author":"J Qian","year":"2023","unstructured":"Qian J, Qiu X, Tan X, Li Q, Chen J, Jiang X (2023) An attentive lstm based approach for adverse drug reactions prediction. Appl Intell 53(5):4875\u20134889","journal-title":"Appl Intell"},{"key":"1963_CR39","doi-asserted-by":"crossref","unstructured":"Luong M-T, Pham H, Manning CD (2015) Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025","DOI":"10.18653\/v1\/D15-1166"},{"key":"1963_CR40","volume":"5","author":"G Zhang","year":"2021","unstructured":"Zhang G, Bai X, Wang Y (2021) Short-time multi-energy load forecasting method based on cnn-seq2seq model with attention mechanism. Mach Learn Appl 5:100064","journal-title":"Mach Learn Appl"},{"key":"1963_CR41","doi-asserted-by":"crossref","unstructured":"Sundermeyer M, Schl\u00fcter R, Ney H (2012) Lstm neural networks for language modeling. In: Thirteenth Annual Conference of the International Speech Communication Association","DOI":"10.21437\/Interspeech.2012-65"},{"key":"1963_CR42","doi-asserted-by":"crossref","unstructured":"Sivaramakrishnan S, Fernandez TF, Babukarthik R, Premalatha S (2022) Forecasting time series data using arima and facebook prophet models. In: Big Data Management in Sensing, pp 47\u201359. River Publishers","DOI":"10.1201\/9781003337355-4"},{"key":"1963_CR43","doi-asserted-by":"crossref","unstructured":"Yifan D, Jialin L, Boxi F (2021) Forecast model of breast cancer diagnosis based on rf-adaboost. In: 2021 International Conference on communications, information system and computer engineering (CISCE), pp 716\u2013719. IEEE","DOI":"10.1109\/CISCE52179.2021.9445847"},{"issue":"3","key":"1963_CR44","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1109\/59.932287","volume":"16","author":"N Amjady","year":"2001","unstructured":"Amjady N (2001) Short-term hourly load forecasting using time-series modeling with peak load estimation capability. IEEE Trans Power Syst 16(3):498\u2013505","journal-title":"IEEE Trans Power Syst"},{"issue":"1","key":"1963_CR45","doi-asserted-by":"publisher","first-page":"128","DOI":"10.3390\/en15010128","volume":"15","author":"D-J Bae","year":"2021","unstructured":"Bae D-J, Kwon B-S, Song K-B (2021) Xgboost-based day-ahead load forecasting algorithm considering behind-the-meter solar pv generation. Energies 15(1):128","journal-title":"Energies"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-01963-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-023-01963-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-01963-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,10]],"date-time":"2024-03-10T17:17:35Z","timestamp":1710091055000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-023-01963-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,11]]},"references-count":45,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["1963"],"URL":"https:\/\/doi.org\/10.1007\/s13042-023-01963-x","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,11]]},"assertion":[{"value":"10 January 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 August 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 September 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}