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Applying support vector machine to predict hourly cooling load in the building

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  1. Chaudhary, Gaurav & New, Joshua & Sanyal, Jibonananda & Im, Piljae & O’Neill, Zheng & Garg, Vishal, 2016. "Evaluation of “Autotune” calibration against manual calibration of building energy models," Applied Energy, Elsevier, vol. 182(C), pages 115-134.
  2. Zhang, Xingxing & Lovati, Marco & Vigna, Ilaria & Widén, Joakim & Han, Mengjie & Gal, Csilla & Feng, Tao, 2018. "A review of urban energy systems at building cluster level incorporating renewable-energy-source (RES) envelope solutions," Applied Energy, Elsevier, vol. 230(C), pages 1034-1056.
  3. Ahmed Salih Mohammed & Panagiotis G. Asteris & Mohammadreza Koopialipoor & Dimitrios E. Alexakis & Minas E. Lemonis & Danial Jahed Armaghani, 2021. "Stacking Ensemble Tree Models to Predict Energy Performance in Residential Buildings," Sustainability, MDPI, vol. 13(15), pages 1-22, July.
  4. Jinrong Wu & Su Nguyen & Damminda Alahakoon & Daswin De Silva & Nishan Mills & Prabod Rathnayaka & Harsha Moraliyage & Andrew Jennings, 2024. "A Comparative Analysis of Machine Learning-Based Energy Baseline Models across Multiple Building Types," Energies, MDPI, vol. 17(6), pages 1-18, March.
  5. Jain, Rishee K. & Smith, Kevin M. & Culligan, Patricia J. & Taylor, John E., 2014. "Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy," Applied Energy, Elsevier, vol. 123(C), pages 168-178.
  6. Wang, Zhe & Hong, Tianzhen & Piette, Mary Ann, 2020. "Building thermal load prediction through shallow machine learning and deep learning," Applied Energy, Elsevier, vol. 263(C).
  7. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A novel improved model for building energy consumption prediction based on model integration," Applied Energy, Elsevier, vol. 262(C).
  8. Muideen Adegoke & Alaka Hafiz & Saheed Ajayi & Razak Olu-Ajayi, 2022. "Application of Multilayer Extreme Learning Machine for Efficient Building Energy Prediction," Energies, MDPI, vol. 15(24), pages 1-21, December.
  9. Carpenter, Joseph & Woodbury, Keith A. & O'Neill, Zheng, 2018. "Using change-point and Gaussian process models to create baseline energy models in industrial facilities: A comparison," Applied Energy, Elsevier, vol. 213(C), pages 415-425.
  10. Milen Balbis-Morejón & Juan J. Cabello-Eras & Javier M. Rey-Hernández & Francisco J. Rey-Martínez, 2021. "Energy Evaluation and Energy Savings Analysis with the 2 Selection of AC Systems in an Educational Building," Sustainability, MDPI, vol. 13(14), pages 1-10, July.
  11. Salari, Ali & Shakibi, Hamid & Alimohammadi, Mahdieh & Naghdbishi, Ali & Goodarzi, Shadi, 2023. "A machine learning approach to optimize the performance of a combined solar chimney-photovoltaic thermal power plant," Renewable Energy, Elsevier, vol. 212(C), pages 717-737.
  12. Gao, Zhikun & Yang, Siyuan & Yu, Junqi & Zhao, Anjun, 2024. "Hybrid forecasting model of building cooling load based on combined neural network," Energy, Elsevier, vol. 297(C).
  13. Jia, Lizhi & Liu, Junjie & Chong, Adrian & Dai, Xilei, 2022. "Deep learning and physics-based modeling for the optimization of ice-based thermal energy systems in cooling plants," Applied Energy, Elsevier, vol. 322(C).
  14. Chia, Yen Yee & Lee, Lam Hong & Shafiabady, Niusha & Isa, Dino, 2015. "A load predictive energy management system for supercapacitor-battery hybrid energy storage system in solar application using the Support Vector Machine," Applied Energy, Elsevier, vol. 137(C), pages 588-602.
  15. Jeong, Kwangbok & Hong, Taehoon & Kim, Jimin & Lee, Jaewook, 2021. "A data-driven approach for establishing a CO2 emission benchmark for a multi-family housing complex using data mining techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
  16. Moazzami, M. & Khodabakhshian, A. & Hooshmand, R., 2013. "A new hybrid day-ahead peak load forecasting method for Iran’s National Grid," Applied Energy, Elsevier, vol. 101(C), pages 489-501.
  17. Koschwitz, D. & Frisch, J. & van Treeck, C., 2018. "Data-driven heating and cooling load predictions for non-residential buildings based on support vector machine regression and NARX Recurrent Neural Network: A comparative study on district scale," Energy, Elsevier, vol. 165(PA), pages 134-142.
  18. Bingchun Liu & Chuanchuan Fu & Arlene Bielefield & Yan Quan Liu, 2017. "Forecasting of Chinese Primary Energy Consumption in 2021 with GRU Artificial Neural Network," Energies, MDPI, vol. 10(10), pages 1-15, September.
  19. Gao, Zhikun & Yu, Junqi & Zhao, Anjun & Hu, Qun & Yang, Siyuan, 2022. "A hybrid method of cooling load forecasting for large commercial building based on extreme learning machine," Energy, Elsevier, vol. 238(PC).
  20. Sun, Chunhua & Zhang, Haixiang & Cao, Shanshan & Xia, Guoqiang & Zhong, Jian & Wu, Xiangdong, 2023. "A hierarchical classifying and two-step training strategy for detection and diagnosis of anormal temperature in district heating system," Applied Energy, Elsevier, vol. 349(C).
  21. Hossein Moayedi & Amir Mosavi, 2021. "Synthesizing Multi-Layer Perceptron Network with Ant Lion Biogeography-Based Dragonfly Algorithm Evolutionary Strategy Invasive Weed and League Champion Optimization Hybrid Algorithms in Predicting He," Sustainability, MDPI, vol. 13(6), pages 1-20, March.
  22. Zhong, Hai & Wang, Jiajun & Jia, Hongjie & Mu, Yunfei & Lv, Shilei, 2019. "Vector field-based support vector regression for building energy consumption prediction," Applied Energy, Elsevier, vol. 242(C), pages 403-414.
  23. Afroz, Zakia & Urmee, Tania & Shafiullah, G.M. & Higgins, Gary, 2018. "Real-time prediction model for indoor temperature in a commercial building," Applied Energy, Elsevier, vol. 231(C), pages 29-53.
  24. Paiho, Satu & Kiljander, Jussi & Sarala, Roope & Siikavirta, Hanne & Kilkki, Olli & Bajpai, Arpit & Duchon, Markus & Pahl, Marc-Oliver & Wüstrich, Lars & Lübben, Christian & Kirdan, Erkin & Schindler,, 2021. "Towards cross-commodity energy-sharing communities – A review of the market, regulatory, and technical situation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
  25. Mateo Jesper & Felix Pag & Klaus Vajen & Ulrike Jordan, 2022. "Heat Load Profiles in Industry and the Tertiary Sector: Correlation with Electricity Consumption and Ex Post Modeling," Sustainability, MDPI, vol. 14(7), pages 1-32, March.
  26. Venkatraj, V. & Dixit, M.K., 2022. "Challenges in implementing data-driven approaches for building life cycle energy assessment: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
  27. Bassamzadeh, Nastaran & Ghanem, Roger, 2017. "Multiscale stochastic prediction of electricity demand in smart grids using Bayesian networks," Applied Energy, Elsevier, vol. 193(C), pages 369-380.
  28. Hyo-Jun Kim & Young-Hum Cho, 2021. "Optimal Control Method of Variable Air Volume Terminal Unit System," Energies, MDPI, vol. 14(22), pages 1-15, November.
  29. Luo, Na & Hong, Tianzhen & Li, Hui & Jia, Ruoxi & Weng, Wenguo, 2017. "Data analytics and optimization of an ice-based energy storage system for commercial buildings," Applied Energy, Elsevier, vol. 204(C), pages 459-475.
  30. Mahmoud Abdelkader Bashery Abbass & Mohamed Hamdy, 2021. "A Generic Pipeline for Machine Learning Users in Energy and Buildings Domain," Energies, MDPI, vol. 14(17), pages 1-30, August.
  31. Wang, Zeyu & Srinivasan, Ravi S., 2017. "A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 796-808.
  32. Zhengrong Li & Yang Si & Qun Zhao & Xiwen Feng, 2023. "A New Method of Building Envelope Thermal Performance Evaluation Considering Window–Wall Correlation," Energies, MDPI, vol. 16(19), pages 1-25, October.
  33. Ma, Dingyuan & Li, Xiaodong & Lin, Borong & Zhu, Yimin, 2023. "An intelligent retrofit decision-making model for building program planning considering tacit knowledge and multiple objectives," Energy, Elsevier, vol. 263(PB).
  34. Kamel, Ehsan & Sheikh, Shaya & Huang, Xueqing, 2020. "Data-driven predictive models for residential building energy use based on the segregation of heating and cooling days," Energy, Elsevier, vol. 206(C).
  35. Yu, Binbin & Li, Jianjing & Liu, Che & Sun, Bo, 2022. "A novel short-term electrical load forecasting framework with intelligent feature engineering," Applied Energy, Elsevier, vol. 327(C).
  36. Guelpa, Elisa & Marincioni, Ludovica & Verda, Vittorio, 2019. "Towards 4th generation district heating: Prediction of building thermal load for optimal management," Energy, Elsevier, vol. 171(C), pages 510-522.
  37. Fan, Cheng & Xiao, Fu & Zhao, Yang, 2017. "A short-term building cooling load prediction method using deep learning algorithms," Applied Energy, Elsevier, vol. 195(C), pages 222-233.
  38. Fu, Guoyin, 2018. "Deep belief network based ensemble approach for cooling load forecasting of air-conditioning system," Energy, Elsevier, vol. 148(C), pages 269-282.
  39. Kusiak, Andrew & Li, Mingyang & Zhang, Zijun, 2010. "A data-driven approach for steam load prediction in buildings," Applied Energy, Elsevier, vol. 87(3), pages 925-933, March.
  40. Abdulrahman Alanezi & Kevin P. Hallinan & Rodwan Elhashmi, 2021. "Using Smart-WiFi Thermostat Data to Improve Prediction of Residential Energy Consumption and Estimation of Savings," Energies, MDPI, vol. 14(1), pages 1-16, January.
  41. Li, Xinyi & Yao, Runming, 2020. "A machine-learning-based approach to predict residential annual space heating and cooling loads considering occupant behaviour," Energy, Elsevier, vol. 212(C).
  42. Konrad Gac & Grzegorz Góra & Maciej Petko & Joanna Iwaniec & Adam Martowicz & Artur Kowalski, 2023. "Modelling of Automated Store Energy Consumption," Energies, MDPI, vol. 16(24), pages 1-23, December.
  43. Geyer, Philipp & Singaravel, Sundaravelpandian, 2018. "Component-based machine learning for performance prediction in building design," Applied Energy, Elsevier, vol. 228(C), pages 1439-1453.
  44. Lara Ramadan & Isam Shahrour & Hussein Mroueh & Fadi Hage Chehade, 2021. "Use of Machine Learning Methods for Indoor Temperature Forecasting," Future Internet, MDPI, vol. 13(10), pages 1-18, September.
  45. Lotta Kannari & Jussi Kiljander & Kalevi Piira & Jouko Piippo & Pekka Koponen, 2021. "Building Heat Demand Forecasting by Training a Common Machine Learning Model with Physics-Based Simulator," Forecasting, MDPI, vol. 3(2), pages 1-13, April.
  46. Jiang, Ben & Li, Yu & Rezgui, Yacine & Zhang, Chengyu & Wang, Peng & Zhao, Tianyi, 2024. "Multi-source domain generalization deep neural network model for predicting energy consumption in multiple office buildings," Energy, Elsevier, vol. 299(C).
  47. Han, Zhezhe & Hossain, Md. Moinul & Wang, Yuwei & Li, Jian & Xu, Chuanlong, 2020. "Combustion stability monitoring through flame imaging and stacked sparse autoencoder based deep neural network," Applied Energy, Elsevier, vol. 259(C).
  48. Jiang, Lai & Yao, Runming & Liu, Kecheng & McCrindle, Rachel, 2017. "An Epistemic-Deontic-Axiologic (EDA) agent-based energy management system in office buildings," Applied Energy, Elsevier, vol. 205(C), pages 440-452.
  49. Zhang, Liang & Wen, Jin & Li, Yanfei & Chen, Jianli & Ye, Yunyang & Fu, Yangyang & Livingood, William, 2021. "A review of machine learning in building load prediction," Applied Energy, Elsevier, vol. 285(C).
  50. Zhicheng Xiao & Lijuan Yu & Huajun Zhang & Xuetao Zhang & Yixin Su, 2023. "HVAC Load Forecasting Based on the CEEMDAN-Conv1D-BiLSTM-AM Model," Mathematics, MDPI, vol. 11(22), pages 1-24, November.
  51. Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
  52. Yafei Zhao & Paolo Vincenzo Genovese & Zhixing Li, 2020. "Intelligent Thermal Comfort Controlling System for Buildings Based on IoT and AI," Future Internet, MDPI, vol. 12(2), pages 1-18, February.
  53. Chalal, Moulay Larbi & Benachir, Medjdoub & White, Michael & Shrahily, Raid, 2016. "Energy planning and forecasting approaches for supporting physical improvement strategies in the building sector: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 761-776.
  54. Tian, Wei & Song, Jitian & Li, Zhanyong & de Wilde, Pieter, 2014. "Bootstrap techniques for sensitivity analysis and model selection in building thermal performance analysis," Applied Energy, Elsevier, vol. 135(C), pages 320-328.
  55. Yuwen You & Zhonghua Wang & Zhihao Liu & Chunmei Guo & Bin Yang, 2024. "Load Prediction of Regional Heat Exchange Station Based on Fuzzy Clustering Based on Fourier Distance and Convolutional Neural Network–Bidirectional Long Short-Term Memory Network," Energies, MDPI, vol. 17(16), pages 1-19, August.
  56. Cameron Francis Assadian & Francis Assadian, 2023. "Data-Driven Modeling of Appliance Energy Usage," Energies, MDPI, vol. 16(22), pages 1-12, November.
  57. Lazos, Dimitris & Sproul, Alistair B. & Kay, Merlinde, 2014. "Optimisation of energy management in commercial buildings with weather forecasting inputs: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 587-603.
  58. Pallonetto, Fabiano & De Rosa, Mattia & D’Ettorre, Francesco & Finn, Donal P., 2020. "On the assessment and control optimisation of demand response programs in residential buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
  59. Kapp, Sean & Choi, Jun-Ki & Hong, Taehoon, 2023. "Predicting industrial building energy consumption with statistical and machine-learning models informed by physical system parameters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 172(C).
  60. Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
  61. Adam Kula & Albert Smalcerz & Maciej Sajkowski & Zygmunt Kamiński, 2021. "Analysis of Office Rooms Energy Consumption Data in Respect to Meteorological and Direct Sun Exposure Conditions," Energies, MDPI, vol. 14(22), pages 1-20, November.
  62. Mohammad Nikoo & Akbar Karimi & Reza Kerachian & Hamed Poorsepahy-Samian & Farhang Daneshmand, 2013. "Rules for Optimal Operation of Reservoir-River-Groundwater Systems Considering Water Quality Targets: Application of M5P Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(8), pages 2771-2784, June.
  63. Kontokosta, Constantine E. & Tull, Christopher, 2017. "A data-driven predictive model of city-scale energy use in buildings," Applied Energy, Elsevier, vol. 197(C), pages 303-317.
  64. Ji, Ying & Xu, Peng & Duan, Pengfei & Lu, Xing, 2016. "Estimating hourly cooling load in commercial buildings using a thermal network model and electricity submetering data," Applied Energy, Elsevier, vol. 169(C), pages 309-323.
  65. Ramya Kuppusamy & Srete Nikolovski & Yuvaraja Teekaraman, 2023. "Review of Machine Learning Techniques for Power Quality Performance Evaluation in Grid-Connected Systems," Sustainability, MDPI, vol. 15(20), pages 1-29, October.
  66. Muhammad Waseem Ahmad & Anthony Mouraud & Yacine Rezgui & Monjur Mourshed, 2018. "Deep Highway Networks and Tree-Based Ensemble for Predicting Short-Term Building Energy Consumption," Energies, MDPI, vol. 11(12), pages 1-21, December.
  67. Ji, Ying & Xu, Peng, 2015. "A bottom-up and procedural calibration method for building energy simulation models based on hourly electricity submetering data," Energy, Elsevier, vol. 93(P2), pages 2337-2350.
  68. Arjunan, Pandarasamy & Poolla, Kameshwar & Miller, Clayton, 2020. "EnergyStar++: Towards more accurate and explanatory building energy benchmarking," Applied Energy, Elsevier, vol. 276(C).
  69. Jawed Mustafa & Fahad Awjah Almehmadi & Saeed Alqaed & Mohsen Sharifpur, 2022. "Building a Sustainable Energy Community: Design and Integrate Variable Renewable Energy Systems for Rural Communities," Sustainability, MDPI, vol. 14(21), pages 1-21, October.
  70. Feng, Yanxiao & Duan, Qiuhua & Chen, Xi & Yakkali, Sai Santosh & Wang, Julian, 2021. "Space cooling energy usage prediction based on utility data for residential buildings using machine learning methods," Applied Energy, Elsevier, vol. 291(C).
  71. Chou, Jui-Sheng & Ngo, Ngoc-Tri, 2016. "Time series analytics using sliding window metaheuristic optimization-based machine learning system for identifying building energy consumption patterns," Applied Energy, Elsevier, vol. 177(C), pages 751-770.
  72. Keshab Raj Dahal & Ankrit Gupta & Nawa Raj Pokhrel, 2024. "Predicting the Direction of NEPSE Index Movement with News Headlines Using Machine Learning," Econometrics, MDPI, vol. 12(2), pages 1-26, June.
  73. Raza, Muhammad Qamar & Nadarajah, Mithulananthan & Ekanayake, Chandima, 2017. "Demand forecast of PV integrated bioclimatic buildings using ensemble framework," Applied Energy, Elsevier, vol. 208(C), pages 1626-1638.
  74. Bordbari, Mohammad Javad & Seifi, Ali Reza & Rastegar, Mohammad, 2018. "Probabilistic energy consumption analysis in buildings using point estimate method," Energy, Elsevier, vol. 142(C), pages 716-722.
  75. Ling, Jihong & Zhang, Bingyang & Dai, Na & Xing, Jincheng, 2023. "Coupling input feature construction methods and machine learning algorithms for hourly secondary supply temperature prediction," Energy, Elsevier, vol. 278(C).
  76. Dai, Yeming & Zhao, Pei, 2020. "A hybrid load forecasting model based on support vector machine with intelligent methods for feature selection and parameter optimization," Applied Energy, Elsevier, vol. 279(C).
  77. Rackes, Adams & Melo, Ana Paula & Lamberts, Roberto, 2016. "Naturally comfortable and sustainable: Informed design guidance and performance labeling for passive commercial buildings in hot climates," Applied Energy, Elsevier, vol. 174(C), pages 256-274.
  78. Sholahudin, S. & Han, Hwataik, 2016. "Simplified dynamic neural network model to predict heating load of a building using Taguchi method," Energy, Elsevier, vol. 115(P3), pages 1672-1678.
  79. Seyedeh Narjes Fallah & Ravinesh Chand Deo & Mohammad Shojafar & Mauro Conti & Shahaboddin Shamshirband, 2018. "Computational Intelligence Approaches for Energy Load Forecasting in Smart Energy Management Grids: State of the Art, Future Challenges, and Research Directions," Energies, MDPI, vol. 11(3), pages 1-31, March.
  80. Hao, Xiaochen & Guo, Tongtong & Huang, Gaolu & Shi, Xin & Zhao, Yantao & Yang, Yue, 2020. "Energy consumption prediction in cement calcination process: A method of deep belief network with sliding window," Energy, Elsevier, vol. 207(C).
  81. Yan Cao & Towhid Pourrostam & Yousef Zandi & Nebojša Denić & Bogdan Ćirković & Alireza Sadighi Agdas & Abdellatif Selmi & Vuk Vujović & Kittisak Jermsittiparsert & Momir Milic, 2021. "RETRACTED ARTICLE: Analyzing the energy performance of buildings by neuro-fuzzy logic based on different factors," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(12), pages 17349-17373, December.
  82. Li, Zhengwei & Han, Yanmin & Xu, Peng, 2014. "Methods for benchmarking building energy consumption against its past or intended performance: An overview," Applied Energy, Elsevier, vol. 124(C), pages 325-334.
  83. Jing, Gang & Cai, Wenjian & Zhang, Xin & Cui, Can & Yin, Xiaohong & Xian, Huacai, 2019. "Modeling, air balancing and optimal pressure set-point selection for the ventilation system with minimized energy consumption," Applied Energy, Elsevier, vol. 236(C), pages 574-589.
  84. Foucquier, Aurélie & Robert, Sylvain & Suard, Frédéric & Stéphan, Louis & Jay, Arnaud, 2013. "State of the art in building modelling and energy performances prediction: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 272-288.
  85. Mat Daut, Mohammad Azhar & Hassan, Mohammad Yusri & Abdullah, Hayati & Rahman, Hasimah Abdul & Abdullah, Md Pauzi & Hussin, Faridah, 2017. "Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1108-1118.
  86. Wang, Zhe & Hong, Tianzhen & Piette, Mary Ann, 2019. "Data fusion in predicting internal heat gains for office buildings through a deep learning approach," Applied Energy, Elsevier, vol. 240(C), pages 386-398.
  87. Ahmad, Muhammad Waseem & Mourshed, Monjur & Rezgui, Yacine, 2018. "Tree-based ensemble methods for predicting PV power generation and their comparison with support vector regression," Energy, Elsevier, vol. 164(C), pages 465-474.
  88. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
  89. Manuel R. Arahal & Manuel G. Ortega & Manuel G. Satué, 2021. "Chiller Load Forecasting Using Hyper-Gaussian Nets," Energies, MDPI, vol. 14(12), pages 1-15, June.
  90. Afroz, Zakia & Shafiullah, GM & Urmee, Tania & Higgins, Gary, 2018. "Modeling techniques used in building HVAC control systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 83(C), pages 64-84.
  91. Muhammad Muhitur Rahman & Syed Masiur Rahman & Md Shafiullah & Md Arif Hasan & Uneb Gazder & Abdullah Al Mamun & Umer Mansoor & Mohammad Tamim Kashifi & Omer Reshi & Md Arifuzzaman & Md Kamrul Islam &, 2022. "Energy Demand of the Road Transport Sector of Saudi Arabia—Application of a Causality-Based Machine Learning Model to Ensure Sustainable Environment," Sustainability, MDPI, vol. 14(23), pages 1-21, December.
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