{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T01:57:12Z","timestamp":1776131832211,"version":"3.50.1"},"reference-count":123,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T00:00:00Z","timestamp":1773964800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100008675","name":"Zayed University","doi-asserted-by":"publisher","award":["R23016"],"award-info":[{"award-number":["R23016"]}],"id":[{"id":"10.13039\/501100008675","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Advanced Engineering Informatics"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.aei.2026.104625","type":"journal-article","created":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T10:23:35Z","timestamp":1774520615000},"page":"104625","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PA","title":["The application of machine learning and deep learning on demand forecasting across time-critical industries: A systematic review"],"prefix":"10.1016","volume":"74","author":[{"given":"Asmaa","family":"Seyam","sequence":"first","affiliation":[]},{"given":"Sujith Samuel","family":"Mathew","sequence":"additional","affiliation":[]},{"given":"May El","family":"Barachi","sequence":"additional","affiliation":[]},{"given":"Cheng","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9403-7140","authenticated-orcid":false,"given":"Jun","family":"Shen","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.aei.2026.104625_b0005","first-page":"1","article-title":"Data analytics in the supply chain management: review of machine learning applications in demand forecasting","volume":"14","author":"Aamer","year":"2020","journal-title":"Operations and Supply Chain Management: an International Journal"},{"issue":"1","key":"10.1016\/j.aei.2026.104625_b0010","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1007\/s12652-020-02761-x","article-title":"Improving time series forecasting using lstm and attention models","volume":"13","author":"Abbasimehr","year":"2022","journal-title":"J. Ambient Intell. Hum. Comput."},{"key":"10.1016\/j.aei.2026.104625_b0015","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.spc.2021.10.001","article-title":"Forecasting of transportation-related energy demand and Co2 Emissions in Turkey with different machine learning algorithms","volume":"29","author":"A\u011fbulut","year":"2022","journal-title":"Sustainable Prod. Consumption"},{"issue":"3","key":"10.1016\/j.aei.2026.104625_b0020","doi-asserted-by":"crossref","first-page":"502","DOI":"10.3390\/forecast6030028","article-title":"Systematic mapping study of sales forecasting: methods, trends, and future directions","volume":"6","author":"Ahaggach","year":"2024","journal-title":"Forecasting"},{"key":"10.1016\/j.aei.2026.104625_b0025","doi-asserted-by":"crossref","first-page":"1008","DOI":"10.1016\/j.energy.2018.07.084","article-title":"Potential of three variant machine-learning models for forecasting district level medium-term and long-term energy demand in smart grid environment","volume":"160","author":"Ahmad","year":"2018","journal-title":"Energy"},{"key":"10.1016\/j.aei.2026.104625_b0030","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.aei.2017.11.002","article-title":"Short-Term Electricity demand forecasting with Mars, Svr and Arima Models using Aggregated demand Data in Queensland, Australia","volume":"35","author":"Al-Musaylh","year":"2018","journal-title":"Adv. Eng. Inf."},{"key":"10.1016\/j.aei.2026.104625_b0035","article-title":"\u2018Short-term electricity demand forecasting using machine learning methods enriched with ground-based climate and Ecmwf Reanalysis Atmospheric Predictors in Southeast Queensland","volume":"113","author":"Al-Musaylh","year":"2019","journal-title":"Australia\u2019, Renewable and Sustainable Energy Reviews"},{"issue":"16","key":"10.1016\/j.aei.2026.104625_b0040","doi-asserted-by":"crossref","first-page":"4231","DOI":"10.3390\/en13164231","article-title":"Data-driven charging demand prediction at public charging stations using supervised machine learning regression methods","volume":"13","author":"Almaghrebi","year":"2020","journal-title":"Energies"},{"key":"10.1016\/j.aei.2026.104625_b0045","article-title":"Air Cargo Transport demand forecasting using Convlstm2d, an artificial neural network architecture approach","volume":"12","author":"Anguita","year":"2023","journal-title":"Case Studies on Transport Policy"},{"key":"10.1016\/j.aei.2026.104625_b0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.cie.2024.110649","article-title":"Application of simulation and machine learning in supply chain management: a synthesis of the literature using the sim-ml literature classification framework","volume":"198","author":"Badakhshan","year":"2024","journal-title":"Comput. Ind. Eng."},{"key":"10.1016\/j.aei.2026.104625_b0055","doi-asserted-by":"crossref","first-page":"49144","DOI":"10.1109\/ACCESS.2018.2867681","article-title":"Empirical mode decomposition based deep learning for electricity demand forecasting","volume":"6","author":"Bedi","year":"2018","journal-title":"IEEE Access"},{"key":"10.1016\/j.aei.2026.104625_b0060","doi-asserted-by":"crossref","first-page":"1312","DOI":"10.1016\/j.apenergy.2019.01.113","article-title":"Deep learning framework to forecast electricity demand","volume":"238","author":"Bedi","year":"2019","journal-title":"Appl. Energy"},{"key":"10.1016\/j.aei.2026.104625_b0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.annals.2021.103255","article-title":"Tourism demand forecasting with time series imaging: a deep learning model","volume":"90","author":"Bi","year":"2021","journal-title":"Ann. Tour. Res."},{"issue":"3","key":"10.1016\/j.aei.2026.104625_b0070","doi-asserted-by":"crossref","first-page":"224","DOI":"10.3390\/w9030224","article-title":"Clustering and support vector regression for water demand forecasting and anomaly detection","volume":"9","author":"Candelieri","year":"2017","journal-title":"Water"},{"key":"10.1016\/j.aei.2026.104625_b0075","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.cor.2018.01.013","article-title":"Tuning hyperparameters of a svm-based water demand forecasting system through parallel global optimization","volume":"106","author":"Candelieri","year":"2019","journal-title":"Comput. Oper. Res."},{"issue":"5","key":"10.1016\/j.aei.2026.104625_b0080","doi-asserted-by":"crossref","first-page":"2546","DOI":"10.3390\/su14052546","article-title":"Performance analysis of machine learning algorithms for energy demand\u2013supply prediction in smart grids","volume":"14","author":"Cebekhulu","year":"2022","journal-title":"Sustainability"},{"issue":"2","key":"10.1016\/j.aei.2026.104625_b0085","doi-asserted-by":"crossref","first-page":"594","DOI":"10.3390\/s23020594","article-title":"A synthetic data generation technique for enhancement of prediction accuracy of electric vehicles demand","volume":"23","author":"Chatterjee","year":"2023","journal-title":"Sensors"},{"issue":"3","key":"10.1016\/j.aei.2026.104625_b0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2024.103699","article-title":"Forecasting tourism demand with search engine data: a hybrid cnn-bilstm model based on boruta feature selection","volume":"61","author":"Chen","year":"2024","journal-title":"Inf. Process. Manag."},{"key":"10.1016\/j.aei.2026.104625_b0095","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2024.133456","article-title":"A novel approach to predict buildings load based on deep learning and non-intrusive load monitoring technique, toward smart building","volume":"312","author":"Cheng","year":"2024","journal-title":"Energy"},{"key":"10.1016\/j.aei.2026.104625_b0100","doi-asserted-by":"crossref","first-page":"e623","DOI":"10.7717\/peerj-cs.623","article-title":"The coefficient of determination R-squared is more informative than Smape, Mae, Mape, Mse and Rmse in regression analysis evaluation","volume":"7","author":"Chicco","year":"2021","journal-title":"PeerJ Comput. Sci."},{"issue":"4","key":"10.1016\/j.aei.2026.104625_b0105","doi-asserted-by":"crossref","first-page":"1712","DOI":"10.3390\/en16041712","article-title":"Forecasting electricity demand by neural networks and definition of inputs by multi-criteria analysis","volume":"16","author":"Deina","year":"2023","journal-title":"Energies"},{"issue":"9","key":"10.1016\/j.aei.2026.104625_b0110","doi-asserted-by":"crossref","first-page":"2242","DOI":"10.3390\/en13092242","article-title":"Energy demand forecasting using deep learning: applications for the French Grid","volume":"13","author":"del Real","year":"2020","journal-title":"Energies"},{"key":"10.1016\/j.aei.2026.104625_b0115","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.ins.2023.01.095","article-title":"A time series attention mechanism based model for tourism demand forecasting","volume":"628","author":"Dong","year":"2023","journal-title":"Inf. Sci."},{"issue":"12","key":"10.1016\/j.aei.2026.104625_b0120","doi-asserted-by":"crossref","first-page":"7743","DOI":"10.1109\/TII.2020.2970165","article-title":"Short-term forecasting of heat demand of buildings for efficient and optimal energy management based on integrated machine learning models","volume":"16","author":"Eseye","year":"2020","journal-title":"IEEE Trans. Ind. Inf."},{"key":"10.1016\/j.aei.2026.104625_b0125","doi-asserted-by":"crossref","first-page":"91463","DOI":"10.1109\/ACCESS.2019.2924685","article-title":"Machine learning based integrated feature selection approach for improved electricity demand forecasting in decentralized energy systems","volume":"7","author":"Eseye","year":"2019","journal-title":"IEEE Access"},{"issue":"2","key":"10.1016\/j.aei.2026.104625_b0130","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1080\/13675567.2020.1803246","article-title":"Machine learning demand forecasting and supply chain performance","volume":"25","author":"Feizabadi","year":"2022","journal-title":"Int J Log Res Appl"},{"issue":"14","key":"10.1016\/j.aei.2026.104625_b0135","doi-asserted-by":"crossref","first-page":"11161","DOI":"10.3390\/su151411161","article-title":"A demand forecasting strategy based on a retrofit architecture for remote monitoring of legacy building circuits","volume":"15","author":"Fernandes","year":"2023","journal-title":"Sustainability"},{"key":"10.1016\/j.aei.2026.104625_b0140","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.apenergy.2016.05.083","article-title":"a novel hybrid algorithm for electricity price and load forecasting in smart grids with demand-side management","volume":"177","author":"Ghasemi","year":"2016","journal-title":"Appl. Energy"},{"key":"10.1016\/j.aei.2026.104625_b0145","doi-asserted-by":"crossref","unstructured":"Ghimire, S, Nguyen-Huy, T, AL-Musaylh, MS, Deo, RC, Casillas-P\u00e9rez, D & Salcedo-Sanz, S 2023, \u2018A Novel Approach Based on Integration of Convolutional Neural Networks and Echo State Network for Daily Electricity Demand Prediction\u2019, Energy, vol. 275, pp. 127430.","DOI":"10.1016\/j.energy.2023.127430"},{"key":"10.1016\/j.aei.2026.104625_b0150","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.apenergy.2018.03.125","article-title":"Machine learning-based thermal response time ahead energy demand prediction for building heating systems","volume":"221","author":"Guo","year":"2018","journal-title":"Appl. Energy"},{"key":"10.1016\/j.aei.2026.104625_b0155","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ijepes.2016.03.001","article-title":"A systematic design of interval type-2 fuzzy logic system using extreme learning machine for electricity load demand forecasting","volume":"82","author":"Hassan","year":"2016","journal-title":"Int. J. Electr. Power Energy Syst."},{"issue":"9","key":"10.1016\/j.aei.2026.104625_b0160","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.1080\/13683500.2020.1770705","article-title":"A novel two-step procedure for tourism demand forecasting","volume":"24","author":"Huang","year":"2021","journal-title":"Curr. Issue Tour."},{"key":"10.1016\/j.aei.2026.104625_b0165","doi-asserted-by":"crossref","first-page":"1757","DOI":"10.1007\/s11269-021-02808-4","article-title":"An ensemble-learning-based method for short-term water demand forecasting","volume":"35","author":"Huang","year":"2021","journal-title":"Water Resour. Manag."},{"issue":"23","key":"10.1016\/j.aei.2026.104625_b0170","doi-asserted-by":"crossref","first-page":"10678","DOI":"10.3390\/su162310678","article-title":"Daily tourism demand forecasting with the itransformer model","volume":"16","author":"Huang","year":"2024","journal-title":"Sustainability"},{"issue":"4","key":"10.1016\/j.aei.2026.104625_b0175","doi-asserted-by":"crossref","first-page":"1420","DOI":"10.1016\/j.ijforecast.2020.02.005","article-title":"Daily retail demand forecasting using Machine Learning with Emphasis on Calendric special days","volume":"36","author":"Huber","year":"2020","journal-title":"Int. J. Forecast."},{"key":"10.1016\/j.aei.2026.104625_b0180","series-title":"In 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT)","first-page":"1","article-title":"Demand forecasting: Literature Review on Various Methodologies","author":"Ingle","year":"2021"},{"key":"10.1016\/j.aei.2026.104625_b0185","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijpe.2021.108315","article-title":"Machine Learning and Optimization Models for Supplier selection and Order Allocation Planning","volume":"242","author":"Islam","year":"2021","journal-title":"Int. J. Prod. Econ."},{"key":"10.1016\/j.aei.2026.104625_b0190","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2024.134040","article-title":"Experiment and Prediction Analysis of thermal Energy Storage for Heat load Balancing in Domestic Hot Water System","volume":"313","author":"Ji","year":"2024","journal-title":"Energy"},{"key":"10.1016\/j.aei.2026.104625_b0195","article-title":"Machine Learning Brent Crude Oil Price forecasts","volume":"11","author":"Jin","year":"2024","journal-title":"Innovation Emerging Technol."},{"issue":"04","key":"10.1016\/j.aei.2026.104625_b0200","doi-asserted-by":"crossref","DOI":"10.1142\/S1752890924500235","article-title":"Machine Learning Coffee Price predictions","volume":"17","author":"Jin","year":"2024","journal-title":"Journal of Uncertain Systems"},{"key":"10.1016\/j.aei.2026.104625_b0205","doi-asserted-by":"crossref","unstructured":"Jin, B & Xu, X 2025a, \u2018China Commodity Price Index (Ccpi) Forecasting Via the Neural Network\u2019, International Journal of Financial Engineering, pp. 1-27.","DOI":"10.1142\/S2424786325500033"},{"issue":"1","key":"10.1016\/j.aei.2026.104625_b0210","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1007\/s13563-024-00472-9","article-title":"Forecasts of Coking Coal Futures Price Indices through Gaussian Process Regressions","volume":"38","author":"Jin","year":"2025","journal-title":"Miner. Econ."},{"key":"10.1016\/j.aei.2026.104625_b0215","doi-asserted-by":"crossref","unstructured":"Jin, B & Xu, X 2025c, \u2018High-Frequency Csi300 Spot and Futures Price Predictions Via the Neural Network\u2019, Journal of Uncertain Systems, pp. 2550008.","DOI":"10.1142\/S1752890925500084"},{"issue":"6","key":"10.1016\/j.aei.2026.104625_b0220","doi-asserted-by":"crossref","first-page":"4971","DOI":"10.1007\/s00521-024-10726-w","article-title":"Machine Learning-based forecasts of Residential Property prices in Hangzhou City, Zhejiang Province, China","volume":"37","author":"Jin","year":"2025","journal-title":"Neural Comput. & Applic."},{"key":"10.1016\/j.aei.2026.104625_b0225","doi-asserted-by":"crossref","unstructured":"Jin, B & Xu, X 2025e, \u2018A Study of Contemporaneous Residential Real Estate Price Causation across Major Jiangsu Province Cities: Methodology Using Vector Error-Correction Models and Directed Acyclic Graphs\u2019, Economics Open, pp. 2550008.","DOI":"10.1142\/S308284142550008X"},{"key":"10.1016\/j.aei.2026.104625_b0230","article-title":"Hybrid Ensemble Intelligent Model based on Wavelet Transform, Swarm Intelligence and Artificial Neural Network for Electricity demand forecasting","volume":"66","author":"Jnr","year":"2021","journal-title":"Sustain. Cities Soc."},{"key":"10.1016\/j.aei.2026.104625_b0235","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1016\/j.jclepro.2019.01.108","article-title":"Relative Evaluation of Regression Tools for Urban Area Electrical Energy demand forecasting","volume":"218","author":"Johannesen","year":"2019","journal-title":"J. Clean. Prod."},{"key":"10.1016\/j.aei.2026.104625_b0240","doi-asserted-by":"crossref","DOI":"10.1016\/j.compeleceng.2022.108358","article-title":"A Hybrid Deep Learning Framework with Cnn and Bi-Directional Lstm for Store Item demand forecasting","volume":"103","author":"Joseph","year":"2022","journal-title":"Comput. Electr. Eng."},{"issue":"24","key":"10.1016\/j.aei.2026.104625_b0245","doi-asserted-by":"crossref","first-page":"7491","DOI":"10.1080\/00207543.2020.1844332","article-title":"Machine Learning for demand forecasting in the Physical Internet: a Case Study of Agricultural Products in Thailand","volume":"59","author":"Kantasa-Ard","year":"2021","journal-title":"Int. J. Prod. Res."},{"issue":"1","key":"10.1016\/j.aei.2026.104625_b0250","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2021.102816","article-title":"Demand forecasting Model using Hotel Clustering Findings for Hospitality Industry","volume":"59","author":"Kaya","year":"2022","journal-title":"Inf. Process. Manag."},{"key":"10.1016\/j.aei.2026.104625_b0255","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1016\/j.trc.2017.10.016","article-title":"Short-Term forecasting of passenger demand under on-demand Ride Services: a Spatio-Temporal Deep Learning Approach","volume":"85","author":"Ke","year":"2017","journal-title":"Transp. Res. Part C Emerging Technol."},{"key":"10.1016\/j.aei.2026.104625_b0260","doi-asserted-by":"crossref","first-page":"116013","DOI":"10.1109\/ACCESS.2020.3003790","article-title":"Effective demand forecasting Model using Business Intelligence Empowered with Machine Learning","volume":"8","author":"Khan","year":"2020","journal-title":"IEEE Access"},{"issue":"2","key":"10.1016\/j.aei.2026.104625_b0265","doi-asserted-by":"crossref","DOI":"10.1016\/j.jik.2023.100355","article-title":"Innovating Knowledge and Information for a Firm-Level Automobile demand Forecast System: a Machine Learning Perspective","volume":"8","author":"Kim","year":"2023","journal-title":"J. Innov. Knowl."},{"issue":"5","key":"10.1016\/j.aei.2026.104625_b0270","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1007\/s10618-025-01120-8","article-title":"Unpacking the Trend: Decomposition as a Catalyst to Enhance Time Series forecasting Models","volume":"39","author":"Kreuzer","year":"2025","journal-title":"Data Min. Knowl. Disc."},{"key":"10.1016\/j.aei.2026.104625_b0275","doi-asserted-by":"crossref","DOI":"10.1016\/j.epsr.2023.109810","article-title":"User-Centric Predictive Demand-Side Management for Nanogrids Via Machine Learning and Multi-Objective Optimization","volume":"225","author":"Kumar","year":"2023","journal-title":"Electr. Pow. Syst. Res."},{"key":"10.1016\/j.aei.2026.104625_b0280","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1016\/j.annals.2019.01.014","article-title":"Tourism demand forecasting: a Deep Learning Approach","volume":"75","author":"Law","year":"2019","journal-title":"Ann. Tour. Res."},{"key":"10.1016\/j.aei.2026.104625_b0285","doi-asserted-by":"crossref","DOI":"10.1016\/j.trc.2022.103984","article-title":"Improving Short-Term Bike Sharing demand Forecast through an Irregular Convolutional Neural Network","volume":"147","author":"Li","year":"2023","journal-title":"Transp. Res. Part C Emerging Technol."},{"issue":"1","key":"10.1016\/j.aei.2026.104625_b0290","doi-asserted-by":"crossref","first-page":"18674","DOI":"10.1038\/s41598-025-03657-6","article-title":"Event Recognition Technology and Short-Term Rockburst Early Warning Model based on Microseismic monitoring and Ensemble Learning","volume":"15","author":"Li","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.aei.2026.104625_b0295","unstructured":"Ling, J & Wu, WB 2025, \u2018Comparative Analysis of Global and Local Probabilistic Time Series Forecasting for Contiguous Spatial Demand Regions\u2019, arXiv preprint arXiv:2509.08214."},{"key":"10.1016\/j.aei.2026.104625_b0300","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2019.100926","article-title":"Development of an Iot-based big Data Platform for Day-Ahead Prediction of Building heating and Cooling Demands","volume":"41","author":"Luo","year":"2019","journal-title":"Adv. Eng. Inf."},{"key":"10.1016\/j.aei.2026.104625_b0305","doi-asserted-by":"crossref","first-page":"146123","DOI":"10.1109\/ACCESS.2021.3123255","article-title":"Food demand Prediction using the Nonlinear Autoregressive Exogenous Neural Network","volume":"9","author":"Lutoslawski","year":"2021","journal-title":"IEEE Access"},{"key":"10.1016\/j.aei.2026.104625_b0310","unstructured":"Malviya, P & Bhandari, V 2024, \u2018A Systematic Study on Effective Demand Prediction Using Machine Learning\u2019, Journal of Integrated Science and Technology, vol. 12, no. 1, pp. 711-."},{"issue":"4","key":"10.1016\/j.aei.2026.104625_b0315","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1001\/jama.2017.19163","article-title":"Preferred Reporting items for a Systematic Review and Meta-Analysis of Diagnostic Test Accuracy Studies: the Prisma-Dta Statement","volume":"319","author":"McInnes","year":"2018","journal-title":"JAMA"},{"key":"10.1016\/j.aei.2026.104625_b0320","doi-asserted-by":"crossref","DOI":"10.1016\/j.jclepro.2022.131852","article-title":"Reducing Fresh fish Waste While Ensuring Availability: demand Forecast using Censored Data and Machine Learning","volume":"359","author":"Migu\u00e9is","year":"2022","journal-title":"J. Clean. Prod."},{"key":"10.1016\/j.aei.2026.104625_b0325","article-title":"Energy demand load forecasting for Electric Vehicle Charging Stations Network based on Convlstm and Biconvlstm Architectures","author":"Mohammad","year":"2023","journal-title":"IEEE Access"},{"key":"10.1016\/j.aei.2026.104625_b0330","series-title":"Demand and Supply Integration : the Key to World-Class demand forecasting, second","author":"Moon","year":"2018"},{"key":"10.1016\/j.aei.2026.104625_b0335","article-title":"Short-Term Electricity demand forecasting Via Variational Autoencoders and batch Training-based Bidirectional Long Short-Term memory","volume":"52","author":"Moradzadeh","year":"2022","journal-title":"Sustainable Energy Technol. Assess."},{"key":"10.1016\/j.aei.2026.104625_b0340","doi-asserted-by":"crossref","DOI":"10.1016\/j.enbuild.2023.113036","article-title":"Deep Learning Hyperparameter Optimization: Application to Electricity and Heat demand Prediction for buildings","volume":"289","author":"Morteza","year":"2023","journal-title":"Energ. Buildings"},{"key":"10.1016\/j.aei.2026.104625_b0345","doi-asserted-by":"crossref","first-page":"147647","DOI":"10.1109\/ACCESS.2020.3015655","article-title":"A Two-Layer Water demand Prediction System in Urban areas based on Micro-Services and Lstm Neural Networks","volume":"8","author":"Nasser","year":"2020","journal-title":"IEEE Access"},{"issue":"19","key":"10.1016\/j.aei.2026.104625_b0350","doi-asserted-by":"crossref","first-page":"11112","DOI":"10.3390\/app131911112","article-title":"Applying Machine Learning in retail demand Prediction\u2014a Comparison of Tree-based Ensembles and Long Short-Term Memory-based Deep Learning","volume":"13","author":"Nasseri","year":"2023","journal-title":"Appl. Sci."},{"issue":"9","key":"10.1016\/j.aei.2026.104625_b0355","doi-asserted-by":"crossref","first-page":"7179","DOI":"10.3390\/su15097179","article-title":"Tourism demand Prediction after Covid-19 with Deep Learning Hybrid Cnn\u2013Lstm\u2014Case Study of Vietnam and Provinces","volume":"15","author":"Nguyen-Da","year":"2023","journal-title":"Sustainability"},{"issue":"6","key":"10.1016\/j.aei.2026.104625_b0360","doi-asserted-by":"crossref","first-page":"e2812","DOI":"10.1002\/jtr.2812","article-title":"Monthly Tourism demand forecasting with Covid\u201019 Impact\u2010Based Hybrid Convolution Neural Network and Gate Recurrent Unit","volume":"26","author":"Nguyen","year":"2024","journal-title":"Int. J. Tour. Res."},{"key":"10.1016\/j.aei.2026.104625_b0365","doi-asserted-by":"crossref","first-page":"no. 9","DOI":"10.1016\/j.heliyon.2023.e19790","article-title":"Forecasting Next-Hour Electricity demand in Small-Scale Territories: evidence from Jordan","volume":"9","author":"Nofal","year":"2023","journal-title":"Heliyon"},{"key":"10.1016\/j.aei.2026.104625_b0370","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijpe.2022.108748","article-title":"Basket Data-Driven Approach for Omnichannel demand forecasting","volume":"257","author":"Omar","year":"2023","journal-title":"Int. J. Prod. Econ."},{"key":"10.1016\/j.aei.2026.104625_b0375","doi-asserted-by":"crossref","DOI":"10.1016\/j.egyai.2023.100267","article-title":"A Data-Driven Framework for Medium-Term Electric Vehicle Charging demand forecasting","volume":"14","author":"Orzechowski","year":"2023","journal-title":"Energy AI"},{"key":"10.1016\/j.aei.2026.104625_b0380","doi-asserted-by":"crossref","DOI":"10.1016\/j.egyai.2021.100121","article-title":"Forecast Electricity demand in Commercial Building with Machine Learning Models to Enable demand Response Programs","volume":"7","author":"Pallonetto","year":"2022","journal-title":"Energy AI"},{"key":"10.1016\/j.aei.2026.104625_b0385","doi-asserted-by":"crossref","DOI":"10.1109\/ACCESS.2023.3266275","article-title":"Time Series forecasting and Modelling of Food demand Supply Chain based on Regressors Analysis","author":"Panda","year":"2023","journal-title":"IEEE Access"},{"key":"10.1016\/j.aei.2026.104625_b0390","doi-asserted-by":"crossref","unstructured":"Parada, R & Sanz, A 2025, \u2018Iot-Integrated Deep Learning for Forecasting and Decision Support in Reservoir Water Management under Drought Conditions\u2019, Internet of Things, pp. 101780.","DOI":"10.1016\/j.iot.2025.101780"},{"key":"10.1016\/j.aei.2026.104625_b0395","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.126586","article-title":"Machine Learning for Master Production Scheduling: Combining Probabilistic forecasting with Stochastic Optimisation","volume":"271","author":"Paull","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.aei.2026.104625_b0400","article-title":"Water and Energy demand forecasting in Large-Scale Water distribution Networks for Irrigation using Open Data and Machine Learning Algorithms","volume":"188","author":"Perea","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.aei.2026.104625_b0405","article-title":"New Memory-based Hybrid Model for Middle-Term Water demand forecasting in Irrigated areas","volume":"284","author":"Perea","year":"2023","journal-title":"Agric Water Manag"},{"key":"10.1016\/j.aei.2026.104625_b0410","doi-asserted-by":"crossref","DOI":"10.1016\/j.envsoft.2020.104633","article-title":"Smart Meters Data for Modeling and forecasting Water demand at the User-Level","volume":"125","author":"Pesantez","year":"2020","journal-title":"Environ. Model. Software"},{"key":"10.1016\/j.aei.2026.104625_b0415","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1016\/j.energy.2018.10.175","article-title":"A Comparison of Models for forecasting the Residential Natural Gas demand of an Urban Area","volume":"167","author":"Poto\u010dnik","year":"2019","journal-title":"Energy"},{"key":"10.1016\/j.aei.2026.104625_b0420","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.ijpe.2018.06.010","article-title":"Agribusiness Time Series forecasting using Wavelet Neural Networks and Metaheuristic Optimization: an Analysis of the soybean Sack Price and Perishable Products demand","volume":"203","author":"Puchalsky","year":"2018","journal-title":"Int. J. Prod. Econ."},{"key":"10.1016\/j.aei.2026.104625_b0425","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.109956","article-title":"Predictive Analytics for demand forecasting: a Deep Learning-based Decision support System","volume":"258","author":"Punia","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.aei.2026.104625_b0430","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2022\/9928836","article-title":"An Approach for demand forecasting in Steel Industries using Ensemble Learning","volume":"2022","author":"Raju","year":"2022","journal-title":"Complexity"},{"issue":"17","key":"10.1016\/j.aei.2026.104625_b0435","doi-asserted-by":"crossref","first-page":"6124","DOI":"10.3390\/en15176124","article-title":"Day-Ahead load demand forecasting in Urban Community Cluster Microgrids using Machine Learning Methods","volume":"15","author":"Rao","year":"2022","journal-title":"Energies"},{"issue":"1","key":"10.1016\/j.aei.2026.104625_b0440","doi-asserted-by":"crossref","first-page":"231","DOI":"10.3390\/su15010231","article-title":"An Optimized Machine Learning Approach for forecasting thermal Energy demand of buildings","volume":"15","author":"Rastbod","year":"2022","journal-title":"Sustainability"},{"key":"10.1016\/j.aei.2026.104625_b0445","article-title":"Stacking Deep Learning and Machine Learning Models for Short-Term Energy Consumption forecasting","volume":"52","author":"Reddy","year":"2022","journal-title":"Adv. Eng. Inf."},{"key":"10.1016\/j.aei.2026.104625_b0450","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2021.126358","article-title":"Probabilistic Urban Water demand forecasting using Wavelet-based Machine Learning Models","volume":"600","author":"Rezaali","year":"2021","journal-title":"J. Hydrol."},{"key":"10.1016\/j.aei.2026.104625_b0455","doi-asserted-by":"crossref","DOI":"10.1016\/j.jclepro.2023.140265","article-title":"Machine Learning Models for Short-Term demand forecasting in Food Catering Services: a solution to Reduce Food Waste","volume":"435","author":"Rodrigues","year":"2024","journal-title":"J. Clean. Prod."},{"key":"10.1016\/j.aei.2026.104625_b0460","doi-asserted-by":"crossref","DOI":"10.1016\/j.clscn.2024.100184","article-title":"Assessment of Barriers Towards a Sustainable and Resilient Poultry Industry Supply Chain: a developing Country viewpoint after Covid-19","volume":"13","author":"Roy","year":"2024","journal-title":"Cleaner Logist. Supply Chain"},{"issue":"1","key":"10.1016\/j.aei.2026.104625_b0465","doi-asserted-by":"crossref","first-page":"3","DOI":"10.3390\/en10010003","article-title":"Deep Neural Network based demand Side Short Term load forecasting","volume":"10","author":"Ryu","year":"2016","journal-title":"Energies"},{"issue":"11","key":"10.1016\/j.aei.2026.104625_b0470","doi-asserted-by":"crossref","first-page":"4499","DOI":"10.3390\/en16114499","article-title":"Forecasting Electricity demand in Turkey using Optimization and Machine Learning Algorithms","volume":"16","author":"Saglam","year":"2023","journal-title":"Energies"},{"key":"10.1016\/j.aei.2026.104625_b0475","doi-asserted-by":"crossref","unstructured":"Seyam, A, EI Barachi, M, Zhang, C, Du, B, Shen, J & Mathew, SS 2024, \u2018Enhancing Resilience and Reducing Waste in Food Supply Chains: A Systematic Review and Future Directions Leveraging Emerging Technologies\u2019, International Journal of Logistics Research and Applications, pp. 1-35.","DOI":"10.1080\/13675567.2024.2406555"},{"key":"10.1016\/j.aei.2026.104625_b0480","doi-asserted-by":"crossref","DOI":"10.1016\/j.clscn.2025.100225","article-title":"A Stacking Ensemble Model for Food demand forecasting: a Preventative Approach to Food Waste Reduction","volume":"15","author":"Seyam","year":"2025","journal-title":"Cleaner Logist. Supply Chain"},{"issue":"20","key":"10.1016\/j.aei.2026.104625_b0485","doi-asserted-by":"crossref","first-page":"3605","DOI":"10.3390\/w15203605","article-title":"A Machine Learning Framework for Enhancing Short-Term Water demand forecasting using Attention-Bilstm Networks Integrated with Xgboost Residual Correction","volume":"15","author":"Shan","year":"2023","journal-title":"Water"},{"issue":"16","key":"10.1016\/j.aei.2026.104625_b0490","doi-asserted-by":"crossref","first-page":"10207","DOI":"10.3390\/su141610207","article-title":"Deep Learning Lstm Recurrent Neural Network Model for Prediction of Electric Vehicle Charging demand","volume":"14","author":"Shanmuganathan","year":"2022","journal-title":"Sustainability"},{"key":"10.1016\/j.aei.2026.104625_b0495","doi-asserted-by":"crossref","DOI":"10.1016\/j.petrol.2021.108979","article-title":"Data-Driven Short-Term Natural Gas demand forecasting with Machine Learning Techniques","volume":"206","author":"Sharma","year":"2021","journal-title":"J. Petrol. Sci. Eng."},{"issue":"9","key":"10.1016\/j.aei.2026.104625_b0500","doi-asserted-by":"crossref","first-page":"3425","DOI":"10.3390\/en15093425","article-title":"Bayesian Optimization Algorithm-based Statistical and Machine Learning Approaches for forecasting Short-Term Electricity demand","volume":"15","author":"Sultana","year":"2022","journal-title":"Energies"},{"key":"10.1016\/j.aei.2026.104625_b0505","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.118502","article-title":"Multi-step Ahead Tourism demand forecasting: the Perspective of the Learning using Privileged Information Paradigm","volume":"210","author":"Sun","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.aei.2026.104625_b0510","article-title":"Influence of Exogenous Factors on Water demand forecasting Models during the Covid-19 period","volume":"117","author":"Talib","year":"2023","journal-title":"Eng. Appl. Artif. Intel."},{"key":"10.1016\/j.aei.2026.104625_b0515","doi-asserted-by":"crossref","DOI":"10.1016\/j.trc.2020.102951","article-title":"Multi-Community Passenger demand Prediction at Region Level based on Spatio-Temporal Graph Convolutional Network","volume":"124","author":"Tang","year":"2021","journal-title":"Transp. Res. Part C Emerging Technol."},{"issue":"24","key":"10.1016\/j.aei.2026.104625_b0520","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2024.e40934","article-title":"Empowering Data-Driven load forecasting by Leveraging Long Short-Term memory Recurrent Neural Networks","volume":"10","author":"Waheed","year":"2024","journal-title":"Heliyon"},{"key":"10.1016\/j.aei.2026.104625_b0525","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.trc.2018.07.013","article-title":"The Station-Free Sharing Bike demand forecasting with a Deep Learning Approach and Large-Scale Datasets","volume":"95","author":"Xu","year":"2018","journal-title":"Transp. Res. Part C Emerging Technol."},{"issue":"14","key":"10.1016\/j.aei.2026.104625_b0530","doi-asserted-by":"crossref","first-page":"2593","DOI":"10.1080\/02664763.2016.1259399","article-title":"Short-Run Price Forecast Performance of Individual and Composite Models for 496 Corn Cash Markets","volume":"44","author":"Xu","year":"2017","journal-title":"J. Appl. Stat."},{"issue":"4","key":"10.1016\/j.aei.2026.104625_b0535","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1093\/erae\/jby036","article-title":"Contemporaneous and Granger Causality among Us Corn Cash and futures prices","volume":"46","author":"Xu","year":"2019","journal-title":"Eur. Rev. Agric. Econ."},{"issue":"4","key":"10.1016\/j.aei.2026.104625_b0540","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1002\/ajae.12041","article-title":"Corn Cash Price forecasting","volume":"102","author":"Xu","year":"2020","journal-title":"Am. J. Agric. Econ."},{"key":"10.1016\/j.aei.2026.104625_b0545","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2021.106120","article-title":"Corn Cash Price forecasting with Neural Networks","volume":"184","author":"Xu","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.aei.2026.104625_b0550","doi-asserted-by":"crossref","DOI":"10.1016\/j.mlwa.2021.100035","article-title":"Individual Time Series and Composite forecasting of the Chinese Stock Index","volume":"5","author":"Xu","year":"2021","journal-title":"Machine Learning with Applications"},{"key":"10.1016\/j.aei.2026.104625_b0555","doi-asserted-by":"crossref","DOI":"10.1016\/j.dajour.2023.100267","article-title":"A Gaussian Process Regression Machine Learning Model for forecasting Retail Property prices with Bayesian Optimizations and Cross-Validation","volume":"8","author":"Xu","year":"2023","journal-title":"Decision Analytics Journal"},{"key":"10.1016\/j.aei.2026.104625_b0560","doi-asserted-by":"crossref","DOI":"10.1016\/j.dajour.2023.100229","article-title":"An Integrated Vector Error Correction and Directed Acyclic Graph Method for investigating Contemporaneous Causalities","volume":"7","author":"Xu","year":"2023","journal-title":"Decision Analytics Journal"},{"key":"10.1016\/j.aei.2026.104625_b0565","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.106870","article-title":"Price forecasts of ten Steel Products using Gaussian Process Regressions","volume":"126","author":"Xu","year":"2023","journal-title":"Eng. Appl. Artif. Intel."},{"issue":"6","key":"10.1016\/j.aei.2026.104625_b0570","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1080\/10941665.2023.2256431","article-title":"Regional Tourism demand forecasting with Spatiotemporal Interactions: a Multivariate Decomposition Deep Learning Model","volume":"28","author":"Yang","year":"2023","journal-title":"Asia Pacific Journal of Tourism Research"},{"key":"10.1016\/j.aei.2026.104625_b0575","doi-asserted-by":"crossref","DOI":"10.1016\/j.compenvurbsys.2020.101521","article-title":"Using Graph Structural Information about Flows to Enhance Short-Term demand Prediction in Bike-Sharing Systems","volume":"83","author":"Yang","year":"2020","journal-title":"Comput. Environ. Urban Syst."},{"issue":"10","key":"10.1016\/j.aei.2026.104625_b0580","doi-asserted-by":"crossref","first-page":"3888","DOI":"10.1109\/TITS.2019.2923964","article-title":"Taxi-based mobility demand formulation and prediction using conditional generative adversarial network-driven learning approaches","volume":"20","author":"Yu","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.aei.2026.104625_b0585","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2023.126878","article-title":"Attention mechanism-based transfer learning model for day-ahead energy demand forecasting of shopping mall buildings","volume":"270","author":"Yuan","year":"2023","journal-title":"Energy"},{"key":"10.1016\/j.aei.2026.104625_b0590","doi-asserted-by":"crossref","unstructured":"Zanfei, A, Brentan, BM, Menapace, A, Righetti, M & Herrera, M 2022, \u2018Graph Convolutional Recurrent Neural Networks for Water Demand Forecasting\u2019, Water Resources Research, vol. 58, no. 7, pp. e2022WR032299.","DOI":"10.1029\/2022WR032299"},{"issue":"4","key":"10.1016\/j.aei.2026.104625_b0595","doi-asserted-by":"crossref","first-page":"1480","DOI":"10.1109\/TITS.2019.2909571","article-title":"Short-term prediction of passenger demand in multi-zone level: temporal convolutional neural network with multi-task learning","volume":"21","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"24","key":"10.1016\/j.aei.2026.104625_b0600","doi-asserted-by":"crossref","first-page":"3573","DOI":"10.3390\/w16243573","article-title":"Short-term water supply forecasting for water treatment plant using temporal multi-scale features","volume":"16","author":"Zhang","year":"2024","journal-title":"Water"},{"key":"10.1016\/j.aei.2026.104625_b0605","first-page":"1","article-title":"Developing a multiview spatiotemporal model based on deep graph neural networks to predict the travel demand by bus","author":"Zhao","year":"2023","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"10.1016\/j.aei.2026.104625_b0610","doi-asserted-by":"crossref","DOI":"10.1016\/j.jobe.2023.106335","article-title":"Forecasting energy demand of pcm integrated residential buildings: a machine learning approach","volume":"70","author":"Zhussupbekov","year":"2023","journal-title":"Journal of Building Engineering"},{"issue":"1","key":"10.1016\/j.aei.2026.104625_b9000","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1016\/j.ijforecast.2020.06.008","article-title":"Recurrent neural networks for time series forecasting: Current status and future directions","volume":"37","author":"Hewamalage","year":"2021","journal-title":"International journal of forecasting"}],"container-title":["Advanced Engineering Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034626003174?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034626003174?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T01:18:41Z","timestamp":1776129521000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1474034626003174"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":123,"alternative-id":["S1474034626003174"],"URL":"https:\/\/doi.org\/10.1016\/j.aei.2026.104625","relation":{},"ISSN":["1474-0346"],"issn-type":[{"value":"1474-0346","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"The application of machine learning and deep learning on demand forecasting across time-critical industries: A systematic review","name":"articletitle","label":"Article Title"},{"value":"Advanced Engineering Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.aei.2026.104625","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"104625"}}