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Natural gas consumption forecast with MARS and CMARS models for residential users

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  1. Ruta Dadeliene & Stanislav Dadelo & Natalija Pozniak & Leonidas Sakalauskas, 2020. "Analysis of top kayakers’ training-intensity distribution and physiological adaptation based on structural modelling," Annals of Operations Research, Springer, vol. 289(2), pages 195-210, June.
  2. Xu, Xiaoxiao & Yu, Hao & Sun, Qiuwen & Tam, Vivian W.Y., 2023. "A critical review of occupant energy consumption behavior in buildings: How we got here, where we are, and where we are headed," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
  3. Magdalena Graczyk-Kucharska & Ayse Özmen & Maciej Szafrański & Gerhard Wilhelm Weber & Marek Golińśki & Małgorzata Spychała, 2020. "Knowledge accelerator by transversal competences and multivariate adaptive regression splines," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 28(2), pages 645-669, June.
  4. Sahraei, Mohammad Ali & Duman, Hakan & Çodur, Muhammed Yasin & Eyduran, Ecevit, 2021. "Prediction of transportation energy demand: Multivariate Adaptive Regression Splines," Energy, Elsevier, vol. 224(C).
  5. Longfeng Zhang & Xin Ma & Hui Zhang & Gaoxun Zhang & Peng Zhang, 2022. "Multi-Step Ahead Natural Gas Consumption Forecasting Based on a Hybrid Model: Case Studies in The Netherlands and the United Kingdom," Energies, MDPI, vol. 15(19), pages 1-26, October.
  6. Wei, Nan & Yin, Lihua & Li, Chao & Li, Changjun & Chan, Christine & Zeng, Fanhua, 2021. "Forecasting the daily natural gas consumption with an accurate white-box model," Energy, Elsevier, vol. 232(C).
  7. Beyca, Omer Faruk & Ervural, Beyzanur Cayir & Tatoglu, Ekrem & Ozuyar, Pinar Gokcin & Zaim, Selim, 2019. "Using machine learning tools for forecasting natural gas consumption in the province of Istanbul," Energy Economics, Elsevier, vol. 80(C), pages 937-949.
  8. Özmen, Ayşe & Ng, Szu Hui, 2025. "Predictive modeling for levelized cost of green ammonia," Applied Energy, Elsevier, vol. 398(C).
  9. Li, Wei & Lu, Can, 2019. "The multiple effectiveness of state natural gas consumption constraint policies for achieving sustainable development targets in China," Applied Energy, Elsevier, vol. 235(C), pages 685-698.
  10. Wei, Nan & Yin, Lihua & Li, Chao & Liu, Jinyuan & Li, Changjun & Huang, Yuanyuan & Zeng, Fanhua, 2022. "Data complexity of daily natural gas consumption: Measurement and impact on forecasting performance," Energy, Elsevier, vol. 238(PC).
  11. Ahmat Khazali Acyl & Flavian Emmanuel Sapnken & Aubin Kinfack Jeutsa & Jean Marie Stevy Sama & Marcel Rodrigue Ewodo-Amougou & Jean Gaston Tamba, 2024. "Forecasting Petroleum Products Consumption in the Chadian Road Transport Sector using Optimised Grey Models," International Journal of Energy Economics and Policy, Econjournals, vol. 14(1), pages 603-611, January.
  12. 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.
  13. Nikseresht, Ali, 2025. "Dynamic hydrogen demand forecasting using hybrid time series models: Insights for renewable energy systems," Renewable Energy, Elsevier, vol. 244(C).
  14. Guo-Feng Fan & An Wang & Wei-Chiang Hong, 2018. "Combining Grey Model and Self-Adapting Intelligent Grey Model with Genetic Algorithm and Annual Share Changes in Natural Gas Demand Forecasting," Energies, MDPI, vol. 11(7), pages 1-21, June.
  15. Lu, Hongfang & Ma, Xin & Azimi, Mohammadamin, 2020. "US natural gas consumption prediction using an improved kernel-based nonlinear extension of the Arps decline model," Energy, Elsevier, vol. 194(C).
  16. Palma, Alessia & Paltrinieri, Andrea & Goodell, John W. & Oriani, Marco Ercole, 2024. "The black box of natural gas market: Past, present, and future," International Review of Financial Analysis, Elsevier, vol. 94(C).
  17. Luo, Keyu & Guo, Qiang & Li, Xiafei, 2022. "Can the return connectedness indices from grey energy to natural gas help to forecast the natural gas returns?," Energy Economics, Elsevier, vol. 109(C).
  18. Tsoumalis, Georgios I. & Bampos, Zafeirios N. & Chatzis, Georgios V. & Biskas, Pandelis N. & Keranidis, Stratos D., 2021. "Minimization of natural gas consumption of domestic boilers with convolutional, long-short term memory neural networks and genetic algorithm," Applied Energy, Elsevier, vol. 299(C).
  19. Kuru Merve & Calis Gulben, 2020. "Application of time series models for heating degree day forecasting," Organization, Technology and Management in Construction, Sciendo, vol. 12(1), pages 2137-2146, January.
  20. Oleksandr Castello & Marina Resta, 2023. "A Machine-Learning-Based Approach for Natural Gas Futures Curve Modeling," Energies, MDPI, vol. 16(12), pages 1-22, June.
  21. Taheri, Nima & Jahani, Hamed & Pishvaee, Mir Saman, 2024. "Modeling sustainable bioethanol supply chain in Australia: A system dynamics approach," Renewable Energy, Elsevier, vol. 227(C).
  22. Chi, Guotai & Dong, Bingjie & Zhou, Ying & Jin, Peng, 2024. "Long-horizon predictions of credit default with inconsistent customers," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
  23. Xiong, Pingping & Li, Kailing & Shu, Hui & Wang, Junjie, 2021. "Forecast of natural gas consumption in the Asia-Pacific region using a fractional-order incomplete gamma grey model," Energy, Elsevier, vol. 237(C).
  24. Laib, Oussama & Khadir, Mohamed Tarek & Mihaylova, Lyudmila, 2019. "Toward efficient energy systems based on natural gas consumption prediction with LSTM Recurrent Neural Networks," Energy, Elsevier, vol. 177(C), pages 530-542.
  25. Akbal, Yıldırım & Ünlü, Kamil Demirberk, 2022. "A univariate time series methodology based on sequence-to-sequence learning for short to midterm wind power production," Renewable Energy, Elsevier, vol. 200(C), pages 832-844.
  26. Soltanisarvestani, A. & Safavi, A.A., 2021. "Modeling unaccounted-for gas among residential natural gas consumers using a comprehensive fuzzy cognitive map," Utilities Policy, Elsevier, vol. 72(C).
  27. Ding, Lili & Zhao, Zhongchao & Wang, Lei, 2022. "Probability density forecasts for natural gas demand in China: Do mixed-frequency dynamic factors matter?," Applied Energy, Elsevier, vol. 312(C).
  28. Stephen J. Lee & Cailinn Drouin, 2025. "Forecasting Residential Heating and Electricity Demand with Scalable, High-Resolution, Open-Source Models," Papers 2505.22873, arXiv.org.
  29. Bernardino, Wilton & Falcão, Rodrigo & Jr., João & Ospina, Raydonal & de Souza, Filipe Costa & Correia, José Jonas Alves, 2025. "A study of asset and liability management applied to Brazilian pension funds," European Journal of Operational Research, Elsevier, vol. 322(3), pages 1059-1076.
  30. Ayşe Özmen & Yuriy Zinchenko & Gerhard-Wilhelm Weber, 2023. "Robust multivariate adaptive regression splines under cross-polytope uncertainty: an application in a natural gas market," Annals of Operations Research, Springer, vol. 324(1), pages 1337-1367, May.
  31. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2022. "Forecasting natural gas consumption using Bagging and modified regularization techniques," Energy Economics, Elsevier, vol. 106(C).
  32. Xi, Xian & Zhou, Jinsheng & Gao, Xiangyun & Liu, Donghui & Zheng, Huiling & Sun, Qingru, 2019. "Impact of changes in crude oil trade network patterns on national economy," Energy Economics, Elsevier, vol. 84(C).
  33. Li, Fengyun & Zheng, Haofeng & Li, Xingmei & Yang, Fei, 2021. "Day-ahead city natural gas load forecasting based on decomposition-fusion technique and diversified ensemble learning model," Applied Energy, Elsevier, vol. 303(C).
  34. Ayşe Özmen, 2023. "Sparse regression modeling for short- and long‐term natural gas demand prediction," Annals of Operations Research, Springer, vol. 322(2), pages 921-946, March.
  35. Wei, Nan & Li, Changjun & Peng, Xiaolong & Li, Yang & Zeng, Fanhua, 2019. "Daily natural gas consumption forecasting via the application of a novel hybrid model," Applied Energy, Elsevier, vol. 250(C), pages 358-368.
  36. Lao, Tongfei & Sun, Yanrui, 2022. "Predicting the production and consumption of natural gas in China by using a new grey forecasting method," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 202(C), pages 295-315.
  37. Du, Jian & Zheng, Jianqin & Liang, Yongtu & Lu, Xinyi & Klemeš, Jiří Jaromír & Varbanov, Petar Sabev & Shahzad, Khurram & Rashid, Muhammad Imtiaz & Ali, Arshid Mahmood & Liao, Qi & Wang, Bohong, 2022. "A hybrid deep learning framework for predicting daily natural gas consumption," Energy, Elsevier, vol. 257(C).
  38. Gnekpe, Christian & Tchuente, Dieudonné & Nyawa, Serge & Dey, Prasanta Kumar, 2024. "Energy Performance of Building Refurbishments: Predictive and Prescriptive AI-based Machine Learning Approaches," Journal of Business Research, Elsevier, vol. 183(C).
  39. Fang, Xichen & Guo, Hongye & Zheng, Kedi & Liu, Shuangquan & Chen, Qixin, 2024. "Real-time capacity cost obligations design in high-renewables energy markets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
  40. Matinfard, Sahar & Yaghoubi, Saeed & Kharaji Manouchehrabadi, Maedeh, 2024. "A coordinated approach for a three-echelon solar-wind energy supply with government intervention," Utilities Policy, Elsevier, vol. 86(C).
  41. Tomasz Cieślik & Piotr Narloch & Adam Szurlej & Krzysztof Kogut, 2022. "Indirect Impact of the COVID-19 Pandemic on Natural Gas Consumption by Commercial Consumers in a Selected City in Poland," Energies, MDPI, vol. 15(4), pages 1-18, February.
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