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Forecasting energy markets using support vector machines

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  1. Thrampoulidis, Emmanouil & Mavromatidis, Georgios & Lucchi, Aurelien & Orehounig, Kristina, 2021. "A machine learning-based surrogate model to approximate optimal building retrofit solutions," Applied Energy, Elsevier, vol. 281(C).
  2. Li, Zheng & Zhou, Bo & Hensher, David A., 2022. "Forecasting automobile gasoline demand in Australia using machine learning-based regression," Energy, Elsevier, vol. 239(PD).
  3. Plakandaras, Vasilios & Gupta, Rangan & Gogas, Periklis & Papadimitriou, Theophilos, 2015. "Forecasting the U.S. real house price index," Economic Modelling, Elsevier, vol. 45(C), pages 259-267.
  4. Colombo, Emilio & Pelagatti, Matteo, 2020. "Statistical learning and exchange rate forecasting," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1260-1289.
  5. F. Cordoni, 2020. "A comparison of modern deep neural network architectures for energy spot price forecasting," Digital Finance, Springer, vol. 2(3), pages 189-210, December.
  6. Wang, Delu & Wang, Yadong & Song, Xuefeng & Liu, Yun, 2018. "Coal overcapacity in China: Multiscale analysis and prediction," Energy Economics, Elsevier, vol. 70(C), pages 244-257.
  7. Emilio, Colombo & Gianfranco, Forte & Roberto, Rossignoli, 2016. "Still crazy after all these years: the returns on carry trade," Working Papers 327, University of Milano-Bicocca, Department of Economics, revised 07 Feb 2016.
  8. Simon Pezzutto & Gianluca Grilli & Stefano Zambotti & Stefan Dunjic, 2018. "Forecasting Electricity Market Price for End Users in EU28 until 2020—Main Factors of Influence," Energies, MDPI, vol. 11(6), pages 1-18, June.
  9. Yixi Xue & Jie Ren & Xiaohang Bi, 2019. "Impact of Influencing Factors on CO 2 Emissions in the Yangtze River Delta during Urbanization," Sustainability, MDPI, vol. 11(15), pages 1-19, August.
  10. Erik Heilmann & Janosch Henze & Heike Wetzel, 2021. "Machine learning in energy forecasts with an application to high frequency electricity consumption data," MAGKS Papers on Economics 202135, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
  11. Baruník, Jozef & Malinská, Barbora, 2016. "Forecasting the term structure of crude oil futures prices with neural networks," Applied Energy, Elsevier, vol. 164(C), pages 366-379.
  12. Alexander Ryota Keeley & Ken’ichi Matsumoto & Kenta Tanaka & Yogi Sugiawan & Shunsuke Managi, 2021. "The Impact of Renewable Energy Generation on the Spot Market Price in Germany: Ex-Post Analysis using Boosting Method," The Energy Journal, , vol. 42(1_suppl), pages 1-22, June.
  13. Benedetti, Miriam & Cesarotti, Vittorio & Introna, Vito & Serranti, Jacopo, 2016. "Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study," Applied Energy, Elsevier, vol. 165(C), pages 60-71.
  14. Emilio Colombo & Gianfranco Forte & Roberto Rossignoli, 2019. "Carry Trade Returns with Support Vector Machines," International Review of Finance, International Review of Finance Ltd., vol. 19(3), pages 483-504, September.
  15. Claudio Monteiro & Ignacio J. Ramirez-Rosado & L. Alfredo Fernandez-Jimenez, 2018. "Probabilistic Electricity Price Forecasting Models by Aggregation of Competitive Predictors," Energies, MDPI, vol. 11(5), pages 1-25, April.
  16. Simon Pezzutto & Reza Fazeli & Matteo De Felice & Wolfram Sparber, 2016. "Future development of the air-conditioning market in Europe: an outlook until 2020," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 5(6), pages 649-669, November.
  17. Zeng, Bo & Li, Chuan, 2016. "Forecasting the natural gas demand in China using a self-adapting intelligent grey model," Energy, Elsevier, vol. 112(C), pages 810-825.
  18. Rubaszek Michal & Karolak Zuzanna & Kwas Marek & Uddin Gazi Salah, 2020. "The role of the threshold effect for the dynamics of futures and spot prices of energy commodities," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(5), pages 1-20, December.
  19. Mengistu, Mulu Getachew & Simane, Belay & Eshete, Getachew & Workneh, Tilahun Seyoum, 2016. "Factors affecting households' decisions in biogas technology adoption, the case of Ofla and Mecha Districts, northern Ethiopia," Renewable Energy, Elsevier, vol. 93(C), pages 215-227.
  20. Tiwari, Aviral Kumar & Sharma, Gagan Deep & Rao, Amar & Hossain, Mohammad Razib & Dev, Dhairya, 2024. "Unraveling the crystal ball: Machine learning models for crude oil and natural gas volatility forecasting," Energy Economics, Elsevier, vol. 134(C).
  21. Jikhan Jeong, 2020. "Identifying Consumer Preferences from User- and Crowd-Generated Digital Footprints on Amazon.com by Leveraging Machine Learning and Natural Language Processing," 2020 Papers pje208, Job Market Papers.
  22. Zuzanna Karolak, 2021. "Energy prices forecasting using nonlinear univariate models," Bank i Kredyt, Narodowy Bank Polski, vol. 52(6), pages 577-598.
  23. 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).
  24. 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).
  25. Leehter Yao & Fazida Hanim Hashim & Chien-Chi Lai, 2020. "Dynamic Residential Energy Management for Real-Time Pricing," Energies, MDPI, vol. 13(10), pages 1-15, May.
  26. Meng, Anbo & Zhu, Jianbin & Yan, Baiping & Yin, Hao, 2024. "Day-ahead electricity price prediction in multi-price zones based on multi-view fusion spatio-temporal graph neural network," Applied Energy, Elsevier, vol. 369(C).
  27. Chuntian Cheng & Bin Luo & Shumin Miao & Xinyu Wu, 2016. "Mid-Term Electricity Market Clearing Price Forecasting with Sparse Data: A Case in Newly-Reformed Yunnan Electricity Market," Energies, MDPI, vol. 9(10), pages 1-22, October.
  28. Manickavasagam, Jeevananthan & Visalakshmi, S. & Apergis, Nicholas, 2020. "A novel hybrid approach to forecast crude oil futures using intraday data," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
  29. Deng, Sinan & Inekwe, John & Smirnov, Vladimir & Wait, Andrew & Wang, Chao, 2024. "Seasonality in deep learning forecasts of electricity imbalance prices," Energy Economics, Elsevier, vol. 137(C).
  30. Cheng, Fangzheng & Li, Tian & Wei, Yi-ming & Fan, Tijun, 2019. "The VEC-NAR model for short-term forecasting of oil prices," Energy Economics, Elsevier, vol. 78(C), pages 656-667.
  31. Wen-Ze Wu & Tao Zhang & Chengli Zheng, 2019. "A Novel Optimized Nonlinear Grey Bernoulli Model for Forecasting China’s GDP," Complexity, Hindawi, vol. 2019, pages 1-10, October.
  32. Duan, Huiming & Pang, Xinyu, 2021. "A multivariate grey prediction model based on energy logistic equation and its application in energy prediction in China," Energy, Elsevier, vol. 229(C).
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