{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T15:16:44Z","timestamp":1780586204825,"version":"3.54.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T00:00:00Z","timestamp":1628640000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T00:00:00Z","timestamp":1628640000000},"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":["Neural Comput & Applic"],"published-print":{"date-parts":[[2022,1]]},"DOI":"10.1007\/s00521-021-06384-x","type":"journal-article","created":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T11:10:03Z","timestamp":1628680203000},"page":"477-491","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":88,"title":["An adaptive backpropagation algorithm for long-term electricity load forecasting"],"prefix":"10.1007","volume":"34","author":[{"given":"Nooriya A.","family":"Mohammed","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5057-4171","authenticated-orcid":false,"given":"Ammar","family":"Al-Bazi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,8,11]]},"reference":[{"issue":"3","key":"6384_CR1","doi-asserted-by":"publisher","first-page":"902","DOI":"10.1016\/j.ejor.2009.01.062","volume":"199","author":"H Hahn","year":"2009","unstructured":"Hahn H, Meyer-Nieberg S, Pickl SW (2009) Electric load forecasting methods: tools for decision making. Eur J Oper Res 199(3):902\u2013907","journal-title":"Eur J Oper Res"},{"issue":"2","key":"6384_CR2","doi-asserted-by":"publisher","first-page":"2664","DOI":"10.1016\/j.asoc.2010.10.015","volume":"11","author":"M Khashei","year":"2011","unstructured":"Khashei M, Bijari M (2011) A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Appl Soft Comput 11(2):2664\u20132675","journal-title":"Appl Soft Comput"},{"key":"6384_CR3","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1016\/j.energy.2018.08.030","volume":"162","author":"NA Mohammed","year":"2018","unstructured":"Mohammed NA (2018) Modelling of unsuppressed electrical demand forecasting in Iraq for long term. Energy 162:354\u2013363","journal-title":"Energy"},{"issue":"8","key":"6384_CR4","doi-asserted-by":"publisher","first-page":"771","DOI":"10.1080\/00908319708908889","volume":"19","author":"S Majumdar","year":"2007","unstructured":"Majumdar S, Shukla PR (2007) Electric load forecasting using artificial neural networks and deficit management. Energy Sources 19(8):771\u2013782","journal-title":"Energy Sources"},{"key":"6384_CR5","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1023\/A:1012046824237","volume":"31","author":"JV Ringwood","year":"2011","unstructured":"Ringwood JV, Bofelli D, Murray FT (2011) Forecasting electricity demand on short, medium and long time scales using neural networks. J Intell Rob Syst 31:129\u2013147","journal-title":"J Intell Rob Syst"},{"key":"6384_CR6","first-page":"1","volume":"6\u20139","author":"C Mihai","year":"2017","unstructured":"Mihai C, Helerea E (2017) (2017) Load forecasting using artificial neural networks for the industrial consumer. International Conference on Modern Power Systems (MPS) 6\u20139:1\u20136","journal-title":"International Conference on Modern Power Systems (MPS)"},{"key":"6384_CR7","doi-asserted-by":"publisher","first-page":"172830","DOI":"10.1109\/ACCESS.2019.2957072","volume":"7","author":"S-M Baek","year":"2019","unstructured":"Baek S-M (2019) Mid-term load pattern forecasting with recurrent artificial neural network. IEEE Access 7:172830\u2013172838","journal-title":"IEEE Access"},{"key":"6384_CR8","doi-asserted-by":"crossref","unstructured":"Mamun MA & Nagasaka K (2004) Artificial neural networks applied to long-term electricity demand forecasting. Fourth International Conference on Hybrid Intelligent Systems (HIS'04). IEEE; 204\u2013209.","DOI":"10.1109\/ICHIS.2004.27"},{"issue":"4","key":"6384_CR9","first-page":"249","volume":"7","author":"L Ghods","year":"2011","unstructured":"Ghods L, Kalantar M (2011) Different methods of long-term electric load demand forecasting a comprehensive review. Iran J Electr Electr Eng 7(4):249\u2013259","journal-title":"Iran J Electr Electr Eng"},{"key":"6384_CR10","unstructured":"Zheng S, Yue Y & Hobbs J Generating (2016) Long-term trajectories using deep hierarchical networks. Advances in Neural Information Processing Systems. 1543\u20131551."},{"key":"6384_CR11","doi-asserted-by":"publisher","first-page":"2960","DOI":"10.1007\/s00521-009-0290-y","volume":"18","author":"OAS Carpinteiro","year":"2009","unstructured":"Carpinteiro OAS, Lima I, Moreira EM, Pinheiro CAM, Seraphim E, Pinto JVL (2009) A hierarchical hybrid neural model with time integrators in long-term load forecasting. Neural Comput Appl 18:2960\u20132965","journal-title":"Neural Comput Appl"},{"key":"6384_CR12","doi-asserted-by":"crossref","unstructured":"Lalis J.T., & Maravillas E. (2014) Dynamic forecasting of electric load consumption using the adaptive multi-layer perceptron(AMLP). International Conference on Humanoid, Nanotechnology, Information Technology, Communication, and Control, Environment, and Management (HNICEM). 11 1\u20137","DOI":"10.1109\/HNICEM.2014.7016237"},{"key":"6384_CR13","doi-asserted-by":"publisher","first-page":"1800","DOI":"10.1109\/TNN.2007.896859","volume":"18","author":"GP Zhang","year":"2007","unstructured":"Zhang GP, Kline DM (2007) Quarterly time-series forecasting with neural networks. IEEE Trans Neural Net 18:1800\u20131814","journal-title":"IEEE Trans Neural Net"},{"key":"6384_CR14","doi-asserted-by":"crossref","unstructured":"Faraji J, Hashemi-Dezaki H, Ketabi A (2020) Multi-year load growth-based optimal planning of grid-connected microgrid considering long-term load demand forecasting: A case study of Tehran, Iran. Sustainable Energy Technologies and Assessments. Volume 42,\u00a0December 2020, 100827","DOI":"10.1016\/j.seta.2020.100827"},{"key":"6384_CR15","unstructured":"Ammar, N., Sulaiman, M., Fateh, A. & Mohamad Nor, A. F. (2018) Long-Term Load Forecasting of Power Systems Using Artificial Neural Network. Journal of Engineering and Applied Sciences. 13."},{"key":"6384_CR16","doi-asserted-by":"publisher","first-page":"3612","DOI":"10.3390\/su12093612","volume":"12","author":"D Solyali","year":"2020","unstructured":"Solyali D (2020) A comparative analysis of machine learning approaches for short-\/long-term electricity load forecasting in cyprus. Sustainability 12:3612","journal-title":"Sustainability"},{"issue":"4","key":"6384_CR17","doi-asserted-by":"publisher","first-page":"1033","DOI":"10.1007\/s10100-018-0531-1","volume":"27","author":"G Nalcaci","year":"2019","unstructured":"Nalcaci G, \u00d6zmen A, Weber G-W (2019) Long-term load forecasting: models based on MARS, ANN and LR methods. CEJOR 27(4):1033\u20131049","journal-title":"CEJOR"},{"issue":"1","key":"6384_CR18","first-page":"266","volume":"3","author":"R Achant","year":"2012","unstructured":"Achant R (2012) Long term electric load forecasting using neural networks and support vector machines. Int J Comput Sci Technol 3(1):266\u2013269","journal-title":"Int J Comput Sci Technol"},{"key":"6384_CR19","doi-asserted-by":"publisher","first-page":"1576","DOI":"10.1016\/j.sbspro.2012.09.1144","volume":"58","author":"G O\u011fcu","year":"2012","unstructured":"O\u011fcu G, Demirel OF, Zaim S (2012) Forecasting electricity consumption with neural networks and support vector regression. Procedia Soc Behav Sci 58:1576\u20131585","journal-title":"Procedia Soc Behav Sci"},{"key":"6384_CR20","doi-asserted-by":"publisher","first-page":"6105","DOI":"10.3390\/en13226105","volume":"13","author":"IS Jahan","year":"2020","unstructured":"Jahan IS, Snasel V, Misak S (2020) Intelligent systems for power load forecasting: a study review. Energies 13:6105","journal-title":"Energies"},{"key":"6384_CR21","doi-asserted-by":"crossref","unstructured":"Kazemzadeh M-R, Amjadian A., Amraee T. (2020). A hybrid data mining-driven algorithm for long-term electric peak load and energy demand forecasting. Energy. Volume 204,\u00a01 August 2020, 117948","DOI":"10.1016\/j.energy.2020.117948"},{"issue":"1","key":"6384_CR22","doi-asserted-by":"publisher","first-page":"1008","DOI":"10.1016\/j.energy.2018.07.084","volume":"160","author":"T Ahmad","year":"2018","unstructured":"Ahmad T, Chen H (2018) Potential of three variant machine-learning models for forecasting district level medium-term and long-term energy demand in smart grid environment. Energy 160(1):1008\u20131020","journal-title":"Energy"},{"key":"6384_CR23","doi-asserted-by":"crossref","unstructured":"Nezzar R.M., Farah N., Khadir M.T., Chouireb L. (2016) Mid-Long Terms Load Forecasting using Multi-Model Artificial Neural Networks. International Journal on Electrical Engineering and Informatics. 8(2).","DOI":"10.15676\/ijeei.2016.8.2.11"},{"key":"6384_CR24","doi-asserted-by":"publisher","first-page":"1033","DOI":"10.1007\/s10100-018-0531-1","volume":"27","author":"G Nalcaci","year":"2019","unstructured":"Nalcaci G, \u00d6zmen A, Weber GW (2019) Long-term load forecasting: models based on MARS, ANN and LR methods. CEJOR 27:1033\u20131049","journal-title":"CEJOR"},{"key":"6384_CR25","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1007\/s00521-003-0390-z","volume":"13","author":"HH Nguyen","year":"2004","unstructured":"Nguyen HH, Chan CW (2004) Multiple neural networks for a long-term time series forecast. Neural Comput Appl 13:90\u201398","journal-title":"Neural Comput Appl"},{"key":"6384_CR26","doi-asserted-by":"publisher","first-page":"2131","DOI":"10.1007\/s00521-017-3171-9","volume":"31","author":"HR Alirezaei","year":"2017","unstructured":"Alirezaei HR, Salami A, Mohammadinodoushan M (2017) A study of hybrid data selection method for a wavelet SVR mid-term load forecasting model. Neural Comput Appl 31:2131\u20132141","journal-title":"Neural Comput Appl"},{"issue":"16","key":"6384_CR27","doi-asserted-by":"publisher","first-page":"562","DOI":"10.3390\/app10165627","volume":"10","author":"AI Almazrouee","year":"2020","unstructured":"Almazrouee AI, Almeshal AM, Almutairi AS, Alenezi MR, Alhajeri SN (2020) Long-term forecasting of electrical loads in kuwait using prophet and holt-winters model. Appl Sci 10(16):562","journal-title":"Appl Sci"},{"key":"6384_CR28","doi-asserted-by":"crossref","unstructured":"Melodi A.O., Adeniyi S.T., Oluwaniyi R.H. (2017) Long Term Load Forecasting for Nigeria\u2019s Electric Power Grid Using Ann And Fuzzy Logic Models. IEEE 3rd International Conference on Electro-Technology for National Development (NIGERCON). 962\u2013968.","DOI":"10.1109\/NIGERCON.2017.8281961"},{"key":"6384_CR29","doi-asserted-by":"crossref","unstructured":"Daneshi H., Shahidehpour M., Choobbari A.L. (2008) Long-term load forecasting in the electricity market. IEEE International Conference on Electro\/Information Technology, 2008.","DOI":"10.1109\/EIT.2008.4554335"},{"key":"6384_CR30","doi-asserted-by":"crossref","unstructured":"Adhiswara R., Abdullah A.G., Mulyadi Y. (2019) Long-term electrical consumption forecasting using Artificial Neural Network (ANN). Journal of Physics: Conference Series, 1402 (2019) 033081.","DOI":"10.1088\/1742-6596\/1402\/3\/033081"},{"key":"6384_CR31","doi-asserted-by":"crossref","unstructured":"Yu H. and Zhang Q. (2010) Application of variable structure artificial neural network for mid-long term load forecasting. 2nd IEEE International Conference on Information Management and Engineering.","DOI":"10.1109\/ICIME.2010.5477756"},{"key":"6384_CR32","doi-asserted-by":"crossref","unstructured":"Masoumi A., Jabari F., Zadeh S.G. Mohammadi-Ivatloo B. (2020) Long-Term Load Forecasting Approach Using Dynamic Feed-Forward Back-Propagation Artificial Neural Network. Optimization of Power System Problems; 233\u2013257","DOI":"10.1007\/978-3-030-34050-6_11"},{"key":"6384_CR33","doi-asserted-by":"crossref","unstructured":"Badran S. & Abouelatta O. (2012) Neural Network Integrated with Regression Methods to Forecast Electrical Load. International Conference on Electrical, Electronics and Biomedical Engineering (ICEEBE'2012) Penang (Malaysia) May 19\u201320, 2012","DOI":"10.1049\/cp.2012.0105"},{"key":"6384_CR34","unstructured":"Hamedmoghadam, H., Joorabloo, N. & Jalili, M. Australia's (2018) Long-term electricity demand forecasting using deep neural networks. arXiv preprint"},{"key":"6384_CR35","doi-asserted-by":"crossref","unstructured":"Nyandwi A., Kumar D. (2020) Neural Network Approach to Short and Long Term Load Forecasting Using Weather Conditioning. International Conference on Electrical and Electronics Engineering (ICE3). 02:.258\u2013263.","DOI":"10.1109\/ICE348803.2020.9122859"},{"key":"6384_CR36","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"},{"key":"6384_CR37","doi-asserted-by":"crossref","unstructured":"Taheri S., Jooshaki M., Moeini-Aghtaie M. (2021). Long-term planning of integrated local energy systems using deep learning algorithms. International Journal of Electrical Power & Energy Systems. 129,\u00a0July 2021, 106855","DOI":"10.1016\/j.ijepes.2021.106855"},{"key":"6384_CR38","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1007\/s12053-020-09879-z","volume":"13","author":"JM Jim\u00e9nez","year":"2020","unstructured":"Jim\u00e9nez JM, Stokes L, Moss C, Yang Q, Livina VN (2020) Modelling energy demand response using long short-term memory neural networks. Energ Effi 13:1263\u20131280","journal-title":"Energ Effi"},{"key":"6384_CR39","doi-asserted-by":"crossref","unstructured":"Agrawal R.K., Muchahary F., Tripathi M.M. (2018). Long-term load forecasting with hourly predictions based on long-short-term-memory networks. 018 IEEE Texas Power and Energy Conference (TPEC).","DOI":"10.1109\/TPEC.2018.8312088"},{"key":"6384_CR40","doi-asserted-by":"crossref","unstructured":"Sangrody H., Zhou N., Tutun S., Khorramdel B., Motalleb M., Sarailoo M. (2018). Long Term Forecasting using Machine Learning Methods. IEEE Power and Energy Conference at Illinois (PECI). 2018.","DOI":"10.1109\/PECI.2018.8334980"},{"key":"6384_CR41","doi-asserted-by":"publisher","first-page":"117197","DOI":"10.1016\/j.energy.2020.117197","volume":"197","author":"JQ Wang","year":"2020","unstructured":"Wang JQ, Du Y, Wang J (2020) LSTM based long-term energy consumption prediction with periodicity. Energy 197:117197","journal-title":"Energy"},{"key":"6384_CR42","doi-asserted-by":"crossref","unstructured":"Vantuch T., Vidal A.G., Ramallo-Gonz\u00e1lez A.P., Skarmeta A.F., Mis\u00e1k S. (2018). Machine learning based electric load forecasting for short and long-term period. 2018 IEEE 4th World Forum on Internet of Things (WF-IoT).","DOI":"10.1109\/WF-IoT.2018.8355123"},{"key":"6384_CR43","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/j.neunet.2019.07.003","volume":"118","author":"A Majumdar","year":"2019","unstructured":"Majumdar A, Megha G (2019) Recurrent transform learning. Neural Netw 118:271\u2013279","journal-title":"Neural Netw"},{"key":"6384_CR44","unstructured":"Hewamalage H., Bergmeir C., and Bandara K. (2019) Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions. Preprint submitted to Elsevier, September 4, 2019."},{"key":"6384_CR45","doi-asserted-by":"crossref","unstructured":"Picton P. (2000). Neural Networks, Second Edition, Palgrave.","DOI":"10.5040\/9781350392434"},{"key":"6384_CR46","doi-asserted-by":"publisher","first-page":"1109","DOI":"10.3390\/su12031109","volume":"12","author":"E Choi","year":"2020","unstructured":"Choi E, Cho S, Kim DK (2020) Power demand forecasting using Long Short-Term Memory (LSTM) deep-learning model for monitoring energy. Sustainability 12:1109","journal-title":"Sustainability"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06384-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06384-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06384-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T09:41:13Z","timestamp":1744191673000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06384-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,11]]},"references-count":46,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["6384"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06384-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,11]]},"assertion":[{"value":"13 December 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 July 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 August 2021","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 confirm no conflict of interest with this submission, an original work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}