IDEAS home Printed from https://ideas.repec.org/r/eee/eneeco/v80y2019icp937-949.html

Using machine learning tools for forecasting natural gas consumption in the province of Istanbul

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
  2. Ali Koç & Serap Ulusam Seçkiner, 2024. "Analysing and forecasting the energy consumption of healthcare facilities in the short and medium term. A case study," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 34(3), pages 165-192.
  3. Li, Zheng & Zhou, Bo & Hensher, David A., 2022. "Forecasting automobile gasoline demand in Australia using machine learning-based regression," Energy, Elsevier, vol. 239(PD).
  4. Pala, Zeydin, 2023. "Comparative study on monthly natural gas vehicle fuel consumption and industrial consumption using multi-hybrid forecast models," Energy, Elsevier, vol. 263(PC).
  5. Zhou, Huimin & Dang, Yaoguo & Yang, Yingjie & Wang, Junjie & Yang, Shaowen, 2023. "An optimized nonlinear time-varying grey Bernoulli model and its application in forecasting the stock and sales of electric vehicles," Energy, Elsevier, vol. 263(PC).
  6. Serge Nyawa & Christian Gnekpe & Dieudonné Tchuente, 2025. "Transparent machine learning models for predicting decisions to undertake energy retrofits in residential buildings," Annals of Operations Research, Springer, vol. 354(1), pages 459-487, November.
  7. Huang, Wenyang & Wang, Huiwen & Qin, Haotong & Wei, Yigang & Chevallier, Julien, 2022. "Convolutional neural network forecasting of European Union allowances futures using a novel unconstrained transformation method," Energy Economics, Elsevier, vol. 110(C).
  8. Reza Lotfi & Mehdi Changizi & Pedram MohajerAnsari & Alireza Hosseini & Zahra Javaheri & Sadia Samar Ali, 2025. "A robust, resilience machine learning with risk approach: a case study of gas consumption," Annals of Operations Research, Springer, vol. 351(1), pages 279-302, August.
  9. 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.
  10. 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).
  11. Jinyuan Liu & Shouxi Wang & Nan Wei & Yi Yang & Yihao Lv & Xu Wang & Fanhua Zeng, 2023. "An Enhancement Method Based on Long Short-Term Memory Neural Network for Short-Term Natural Gas Consumption Forecasting," Energies, MDPI, vol. 16(3), pages 1-14, January.
  12. Gechert, Sebastian & Mey, Bianka & Prante, Franz & Schäfer, Teresa, 2025. "The Price Elasticity of Heating and Cooling Energy Demand," OSF Preprints 4sjy5_v2, Center for Open Science.
  13. Zeynep Ceylan, 2020. "Assessment of agricultural energy consumption of Turkey by MLR and Bayesian optimized SVR and GPR models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 944-956, September.
  14. 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).
  15. Saurabh Maheshwari & Niti Nandini Chatnani, 2025. "Applications of Artificial Intelligence and Machine Learning-based Supervisory Technology in Financial Markets Surveillance: A Review of Literature," FIIB Business Review, , vol. 14(5), pages 586-604, October.
  16. Erokhin, Dmitry & Zagler, Martin, 2024. "Who will sign a double tax treaty next? A prediction based on economic determinants and machine learning algorithms," Economic Modelling, Elsevier, vol. 139(C).
  17. Wang, Bin & Wang, Jun, 2020. "Energy futures and spots prices forecasting by hybrid SW-GRU with EMD and error evaluation," Energy Economics, Elsevier, vol. 90(C).
  18. Xu, Guangyue & Chen, Yaqiang & Yang, Mengge & Li, Shuang & Marma, Kyaw Jaw Sine, 2023. "An outlook analysis on China's natural gas consumption forecast by 2035: Applying a seasonal forecasting method," Energy, Elsevier, vol. 284(C).
  19. Zeng, Sheng & Su, Bin & Zhang, Minglong & Gao, Yuan & Liu, Jun & Luo, Song & Tao, Qingmei, 2021. "Analysis and forecast of China's energy consumption structure," Energy Policy, Elsevier, vol. 159(C).
  20. 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).
  21. Li, Jiaman & Dong, Xiucheng & Jiang, Qingzhe & Dong, Kangyin & Liu, Guixian, 2021. "Natural gas trade network of countries and regions along the belt and road: Where to go in the future?," Resources Policy, Elsevier, vol. 71(C).
  22. Gao, Tian & Niu, Dongxiao & Ji, Zhengsen & Sun, Lijie, 2022. "Mid-term electricity demand forecasting using improved variational mode decomposition and extreme learning machine optimized by sparrow search algorithm," Energy, Elsevier, vol. 261(PB).
  23. Yousaf Raza, Muhammad & Lin, Boqiang, 2022. "Natural gas consumption, energy efficiency and low carbon transition in Pakistan," Energy, Elsevier, vol. 240(C).
  24. Haoran Zhao & Sen Guo, 2021. "Uncertain Interval Forecasting for Combined Electricity-Heat-Cooling-Gas Loads in the Integrated Energy System Based on Multi-Task Learning and Multi-Kernel Extreme Learning Machine," Mathematics, MDPI, vol. 9(14), pages 1-32, July.
  25. Xia, Lin & Ren, Youyang & Wang, Yuhong & Pan, Yangyang & Fu, Yiyang, 2024. "Forecasting China's renewable energy consumption using a novel dynamic fractional-order discrete grey multi-power model," Renewable Energy, Elsevier, vol. 233(C).
  26. Wang, Jia & Wang, Xinyi & Wang, Xu, 2024. "International oil shocks and the volatility forecasting of Chinese stock market based on machine learning combination models," The North American Journal of Economics and Finance, Elsevier, vol. 70(C).
  27. Oana Vlăduţ & George Eduard Grigore & Dumitru Alexandru Bodislav & Gabriel Ilie Staicu & Raluca Iuliana Georgescu, 2024. "Analysing the Connection between Economic Growth, Conventional Energy, and Renewable Energy: A Comparative Analysis of the Caspian Countries," Energies, MDPI, vol. 17(1), pages 1-30, January.
  28. Wen, Kai & Jiao, Jianfeng & Zhao, Kang & Yin, Xiong & Liu, Yuan & Gong, Jing & Li, Cuicui & Hong, Bingyuan, 2023. "Rapid transient operation control method of natural gas pipeline networks based on user demand prediction," Energy, Elsevier, vol. 264(C).
  29. Chowdhury, Mohammad Ashraful Ferdous & Abdullah, Mohammad & Abakah, Emmanuel Joel Aikins & Tiwari, Aviral Kumar, 2025. "Geopolitical risk and energy market tail risk forecasting: An explainable machine learning approach," Journal of Commodity Markets, Elsevier, vol. 39(C).
  30. K, Ashin Nishan M & ASHIQ, MUHAMMED V, 2019. "Role of Energy use in the Prediction of CO2 Emissions and Growth in India: An Application of Artificial Neural Networks (ANN)," SocArXiv gkpbu, Center for Open Science.
  31. SeyedAli Ghahari & Cesar Queiroz & Samuel Labi & Sue McNeil, 2021. "Cluster Forecasting of Corruption Using Nonlinear Autoregressive Models with Exogenous Variables (NARX)—An Artificial Neural Network Analysis," Sustainability, MDPI, vol. 13(20), pages 1-20, October.
  32. Feng, Qianqian & Sun, Xiaolei & Hao, Jun & Li, Jianping, 2021. "Predictability dynamics of multifactor-influenced installed capacity: A perspective of country clustering," Energy, Elsevier, vol. 214(C).
  33. 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).
  34. Jiang, Jianming & Liang, Zhenxia & Gan, Yingpan & Ban, Yandong, 2026. "A novel structured discrete grey Gompertz multivariable prediction model and its application," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 240(C), pages 208-225.
  35. Li, Hui & Duan, Huiming & Song, Yuxin & Wang, Xingwu, 2025. "A novel conformable fractional logistic grey model and its application to natural gas and electricity consumption in China," Renewable Energy, Elsevier, vol. 243(C).
  36. Guan, Keqin & Gong, Xu, 2023. "A new hybrid deep learning model for monthly oil prices forecasting," Energy Economics, Elsevier, vol. 128(C).
  37. M. G. Sukharev & I. V. Tverskoy & G. A. Suraev, 2026. "Theory and Practice of Gas Consumption Forecasting in Planning the Development of Regional Gas Supply Systems," Studies on Russian Economic Development, Springer, vol. 37(2), pages 246-256, April.
  38. Dahiru A Bala & Mohammed Shuaibu, 2024. "Forecasting United Kingdom's energy consumption using machine learning and hybrid approaches," Energy & Environment, , vol. 35(3), pages 1493-1531, May.
  39. Wang, Qi & Suo, Ruixia & Han, Qiutong, 2024. "A study on natural gas consumption forecasting in China using the LMDI-PSO-LSTM model: Factor decomposition and scenario analysis," Energy, Elsevier, vol. 292(C).
  40. Lu, Fei & Ma, Feng & Li, Pan & Huang, Dengshi, 2022. "Natural gas volatility predictability in a data-rich world," International Review of Financial Analysis, Elsevier, vol. 83(C).
  41. 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).
  42. Zhang, Xiaokong & Chai, Jian & Tian, Lingyue & Yang, Ying & Zhang, Zhe George & Pan, Yue, 2023. "Forecast and structural characteristics of China's oil product consumption embedded in bottom-line thinking," Energy, Elsevier, vol. 278(PA).
  43. 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).
  44. Nguyen, Quyen & Diaz-Rainey, Ivan & Kuruppuarachchi, Duminda, 2021. "Predicting corporate carbon footprints for climate finance risk analyses: A machine learning approach," Energy Economics, Elsevier, vol. 95(C).
  45. Kamran Hassanpouri Baesmat & Zeinab Farrokhi & Grzegorz Chmaj & Emma E. Regentova, 2025. "Parallel Multi-Model Energy Demand Forecasting with Cloud Redundancy: Leveraging Trend Correction, Feature Selection, and Machine Learning," Forecasting, MDPI, vol. 7(2), pages 1-18, May.
  46. 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.
  47. Svoboda, Radek & Kotik, Vojtech & Platos, Jan, 2021. "Short-term natural gas consumption forecasting from long-term data collection," Energy, Elsevier, vol. 218(C).
  48. 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).
  49. 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).
  50. Xin Guan & Xiangyi Lu & Yang Wen, 2022. "Is China’s Natural Gas Consumption Converging? Empirical Research Based on Spatial Econometrics," Energies, MDPI, vol. 15(24), pages 1-13, December.
  51. 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.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.