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Machine learning and oil price point and density forecasting
Costa, Alexandre Bonnet R.; Ferreira, Pedro Cavalcanti; … - In: Energy economics 102 (2021), pp. 1-21
Persistent link: https://www.econbiz.de/10013162181
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Predicting energy poverty with combinations of remote-sensing and socioeconomic survey data in India : evidence from machine learning
Wang, Hanjie; Maruejols, Lucie; Yu, Xiaohua - In: Energy economics 102 (2021), pp. 1-13
Persistent link: https://www.econbiz.de/10013162202
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Predicting corporate carbon footprints for climate finance risk analyses : a machine learning approach
Nguyen, Quyen; Diaz-Rainey, Ivan; Kuruppuarachchi, Duminda - In: Energy economics 95 (2021), pp. 1-18
Persistent link: https://www.econbiz.de/10012816864
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Won't get fooled again : a supervised machine learning approach for screening gasoline cartels
Silveira, Douglas; Vasconcelos, Silvinha Pinto; … - In: Energy economics 105 (2022), pp. 1-18
Persistent link: https://www.econbiz.de/10013201809
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Machine learning in energy economics and finance: a review
Ghoddusi, Hamed; Creamer Guillén, Germán; Rafizadeh, Nima - In: Energy economics 81 (2019), pp. 709-727
Persistent link: https://www.econbiz.de/10012172954
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Using machine learning tools for forecasting natural gas consumption in the province of Istanbul
Beyca, Ömer Faruk; Ervural, Beyzanur Cayir; Tatoğlu, Ekrem - In: Energy economics 80 (2019), pp. 937-949
Persistent link: https://www.econbiz.de/10012173755
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Toward high-resolution projection of electricity prices : a machine learning approach to quantifying the effects of high fuel and CO2 prices
Madadkhani, Shiva; Ikonnikova, Svetlana - In: Energy economics 129 (2024), pp. 1-18
Persistent link: https://www.econbiz.de/10014558952
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Forecasting the realized variance of oil-price returns using machine learning : is there a role for U.S. state-level uncertainty?
Çepni, Oğuzhan; Gupta, Rangan; Pienaar, Daniel; … - In: Energy economics 114 (2022), pp. 1-14
Persistent link: https://www.econbiz.de/10013477404
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Using machine learning to model technological heterogeneity in carbon emission efficiency evaluation : the case of China's cities
Wang, Ailun; Hu, Shuo; Li, Jianglong - In: Energy economics 114 (2022), pp. 1-15
Persistent link: https://www.econbiz.de/10013477406
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Using machine learning to select variables in data envelopment analysis : simulations and application using electricity distribution data
Duras, Toni; Javed, Farrukh; Månsson, Kristofer; … - In: Energy economics 120 (2023), pp. 1-10
Persistent link: https://www.econbiz.de/10014284003