{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T19:02:24Z","timestamp":1772910144696,"version":"3.50.1"},"reference-count":41,"publisher":"Tech Science Press","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CMC"],"published-print":{"date-parts":[[2025]]},"DOI":"10.32604\/cmc.2025.061400","type":"journal-article","created":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T05:08:41Z","timestamp":1743484121000},"page":"3553-3583","source":"Crossref","is-referenced-by-count":2,"title":["Utilizing Machine Learning and SHAP Values for Improved and Transparent Energy Usage Predictions"],"prefix":"10.32604","volume":"83","author":[{"given":"Faisal Ghazi","family":"Beshaw","sequence":"first","affiliation":[]},{"given":"Thamir Hassan","family":"Atyia","sequence":"additional","affiliation":[]},{"given":"Mohd Fadzli Mohd","family":"Salleh","sequence":"additional","affiliation":[]},{"given":"Mohamad Khairi","family":"Ishak","sequence":"additional","affiliation":[]},{"given":"Abdul Sattar","family":"Din","sequence":"additional","affiliation":[]}],"member":"17807","published-online":{"date-parts":[[2025]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"111295","DOI":"10.1016\/j.enpol.2020.111295","article-title":"Renewable energy consumption and economic growth nexus: evidence from a threshold model","volume":"139","author":"Chen","year":"2020","journal-title":"Energy Policy"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"1903242","DOI":"10.1002\/aenm.201903242","article-title":"A critical review of machine learning of energy materials","volume":"10","author":"Chen","year":"2020","journal-title":"Adv Energy Mater"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"125834","DOI":"10.1016\/j.jclepro.2021.125834","article-title":"Artificial intelligence in sustainable energy industry: status Quo, challenges and opportunities","volume":"289","author":"Ahmad","year":"2021","journal-title":"J Clean Prod"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"113045","DOI":"10.1016\/j.rser.2022.113045","article-title":"Predicting industrial building energy consumption with statistical and machine-learning models informed by physical system parameters","volume":"172","author":"Kapp","year":"2023","journal-title":"Renew Sustain Energy Rev"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1038\/s41597-023-02177-0","article-title":"An inventory of greenhouse gas emissions due to natural gas pipeline incidents in the United States and Canada from, 1980s, to 2021","volume":"10","author":"Lu","year":"2023","journal-title":"Sci Data"},{"key":"ref6","author":"Rightor","year":"2020","journal-title":"Research report"},{"key":"ref7","doi-asserted-by":"crossref","first-page":"5171","DOI":"10.3390\/en13195171","article-title":"Machine learning modeling for energy consumption of residential and commercial sectors","volume":"13","author":"Nabavi","year":"2020","journal-title":"Energies"},{"key":"ref8","series-title":"IECON 2016\u201442nd Annual Conference of the IEEE Industrial Electronics Society","first-page":"7046","article-title":"Building energy load forecasting using deep neural networks","author":"Marino","year":"2016 Oct 23\u201326"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"2830","DOI":"10.1002\/qj.3410","article-title":"Predicting weather forecast uncertainty with machine learning","volume":"144","author":"Scher","year":"2018","journal-title":"Quart J R Meteoro Soc"},{"key":"ref10","first-page":"5005","article-title":"Alzheimer disease detection empowered with transfer learning","volume":"70","author":"Ghazal","year":"2022","journal-title":"Comput Mater Contin"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"71","DOI":"10.3390\/a15020071","article-title":"A review of deep learning algorithms and their applications in healthcare","volume":"15","author":"Abdel-Jaber","year":"2022","journal-title":"Algorithms"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"539","DOI":"10.32604\/iasc.2022.019658","article-title":"Energy demand forecasting using fused machine learning approaches","volume":"31","author":"Ghazal","year":"2022","journal-title":"Intell Autom Soft Comput"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"11","DOI":"10.32604\/iasc.2023.030125","article-title":"Early detection of autism in children using transfer learning","volume":"36","author":"Ghazal","year":"2023","journal-title":"Intell Autom Soft Comput"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/j.eij.2022.03.003","article-title":"Smart cities: fusion-based intelligent traffic congestion control system for vehicular networks using machine learning techniques","volume":"23","author":"Saleem","year":"2022","journal-title":"Egypt Inform J"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"205","DOI":"10.3390\/a15060205","article-title":"A review: machine learning for combinatorial optimization problems in energy areas","volume":"15","author":"Yang","year":"2022","journal-title":"Algorithms"},{"key":"ref16","series-title":"Proceedings of the 8th International Conference on Informatics, Environment, Energy and Applications","first-page":"268","article-title":"Electricity demand forecasting in buildings based on ARIMA and ARX models","author":"Kandananond","year":"2019 Mar 16\u201319"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.apenergy.2014.12.019","article-title":"Modeling and forecasting energy consumption for heterogeneous buildings using a physical-statistical approach","volume":"144","author":"L\u00fc","year":"2015","journal-title":"Appl Energy"},{"key":"ref18","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.rser.2018.02.002","article-title":"Forecasting methods in energy planning models","volume":"88","author":"Debnath","year":"2018","journal-title":"Renew Sustain Energy Rev"},{"key":"ref19","doi-asserted-by":"crossref","first-page":"035358","DOI":"10.1088\/2631-8695\/ad7783","article-title":"Comparative analysis of optimization approaches for combined economic emission dispatch\u2014a comprehensive review","volume":"6","author":"Khlaif","year":"2024","journal-title":"Eng Res Express"},{"key":"ref20","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.enbuild.2019.05.031","article-title":"Predictive modelling of building energy consumption based on a hybrid nature-inspired optimization algorithm","volume":"196","author":"Goudarzi","year":"2019","journal-title":"Energy Build"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"114820","DOI":"10.1016\/j.eswa.2021.114820","article-title":"Machine learning for industrial applications: a comprehensive literature review","volume":"175","author":"Bertolini","year":"2021","journal-title":"Expert Syst Appl"},{"key":"ref22","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.3390\/en12071301","article-title":"State of the art of machine learning models in energy systems, a systematic review","volume":"12","author":"Mosavi","year":"2019","journal-title":"Energies"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1109\/TIE.2018.2807414","article-title":"Early fault detection of machine tools based on deep learning and dynamic identification","volume":"66","author":"Luo","year":"2019","journal-title":"IEEE Trans Ind Electron"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"103380","DOI":"10.1016\/j.compind.2020.103380","article-title":"A survey of machine-learning techniques for condition monitoring and predictive maintenance of bearings in grinding machines","volume":"125","author":"Schwendemann","year":"2021","journal-title":"Comput Ind"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"839","DOI":"10.3390\/s23020839","article-title":"On-device intelligence for malfunction detection of water pump equipment in agricultural premises: feasibility and experimentation","volume":"23","author":"Loukatos","year":"2023","journal-title":"Sensors"},{"key":"ref26","doi-asserted-by":"crossref","first-page":"9249387","DOI":"10.1155\/2021\/9249387","article-title":"Deep learning enhanced solar energy forecasting with AI-driven IoT","volume":"2021","author":"Zhou","year":"2021","journal-title":"Wirel Commun Mob Comput"},{"key":"ref27","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.neucom.2020.01.124","article-title":"Predicting energy cost of public buildings by artificial neural networks, CART, and random forest","volume":"439","author":"Zeki\u0107-Su\u0161ac","year":"2021","journal-title":"Neurocomputing"},{"key":"ref28","unstructured":"Blasch E, Li H, Ma Z, Weng Y. The powerful use of AI in the energy sector: intelligent forecasting. arXiv: 2111.02026. 2021."},{"key":"ref29","doi-asserted-by":"crossref","first-page":"111799","DOI":"10.1016\/j.enconman.2019.111799","article-title":"A review of deep learning for renewable energy forecasting","volume":"198","author":"Wang","year":"2019","journal-title":"Energy Convers Manage"},{"key":"ref30","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1016\/j.enconman.2018.11.074","article-title":"Generative adversarial networks and convolutional neural networks based weather classification model for day ahead short-term photovoltaic power forecasting","volume":"181","author":"Wang","year":"2019","journal-title":"Energy Convers Manage"},{"key":"ref31","doi-asserted-by":"crossref","first-page":"143423","DOI":"10.1109\/ACCESS.2020.3014241","article-title":"A deep learning based hybrid framework for day-ahead electricity price forecasting","volume":"8","author":"Zhang","year":"2020","journal-title":"IEEE Access"},{"key":"ref32","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.energy.2019.05.230","article-title":"Predicting residential energy consumption using CNN-LSTM neural networks","volume":"182","author":"Kim","year":"2019","journal-title":"Energy"},{"key":"ref33","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1016\/j.energy.2019.01.075","article-title":"Optimal load dispatch of community microgrid with deep learning based solar power and load forecasting","volume":"171","author":"Wen","year":"2019","journal-title":"Energy"},{"key":"ref34","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1109\/TSG.2017.2753802","article-title":"Short-term residential load forecasting based on LSTM recurrent neural network","volume":"10","author":"Kong","year":"2017","journal-title":"IEEE Trans Smart Grid"},{"key":"ref35","first-page":"563","article-title":"Load prediction methods using machine learning for home energy management systems based on human behavior patterns recognition","volume":"6","author":"Fan","year":"2020","journal-title":"CSEE J Power Energy Syst"},{"key":"ref36","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1007\/s42452-020-2024-9","article-title":"Machine learning for energy consumption prediction and scheduling in smart buildings","volume":"2","author":"Bourhnane","year":"2020","journal-title":"SN Appl Sci"},{"key":"ref37","doi-asserted-by":"crossref","first-page":"121082","DOI":"10.1016\/j.jclepro.2020.121082","article-title":"Predicting energy consumption in multiple buildings using machine learning for improving energy efficiency and sustainability","volume":"260","author":"Pham","year":"2020","journal-title":"J Clean Prod"},{"key":"ref38","series-title":"2019 Eleventh International Conference on Ubiquitous and Future Networks (ICUFN)","first-page":"66","article-title":"Machine learning for smart energy monitoring of home appliances using IoT","author":"Rashid","year":"2019 July 2\u20135"},{"key":"ref39","doi-asserted-by":"crossref","first-page":"566","DOI":"10.52783\/jes.2983","article-title":"Review artificial intelligence applications in renewable energy systems integration","volume":"20","author":"Bishaw","year":"2024","journal-title":"J Electr Syst"},{"key":"ref40","series-title":"2019 IEEE Congress on Evolutionary Computation (CEC)","first-page":"1510","article-title":"Particle swarm optimization-based CNN-LSTM networks for forecasting energy consumption","author":"Kim","year":"2019 Jun 10\u201313"},{"key":"ref41","series-title":"AIP Conference Proceedings","doi-asserted-by":"crossref","DOI":"10.1063\/5.0182370","article-title":"An efficient wireless monitoring system for photovoltaic panels using bluetooth technology","author":"Khaleel","year":"2024"}],"container-title":["Computers, Materials &amp; Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/cdn.techscience.cn\/files\/cmc\/2025\/TSP_CMC-83-2\/TSP_CMC_61400\/TSP_CMC_61400.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T01:06:40Z","timestamp":1763341600000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v83n2\/60553"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":41,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2025.061400","relation":{},"ISSN":["1546-2226"],"issn-type":[{"value":"1546-2226","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}