Machine learning in energy economics and finance: A review

被引：286
作者：
Ghoddusi, Hamed [1 ]
Creamer, German G. [1 ]
Rafizadeh, Nima
机构：
[1] Stevens Inst Technol, Sch Business, Hoboken, NJ 07030 USA
来源：
ENERGY ECONOMICS | 2019年 / 81卷
关键词：
Machine learning; Energy markets; Energy finance; Support Vector Machine; Artificial Neural Network; Forecasting; Crude oil; Electricity price; CRUDE-OIL PRICE; ARTIFICIAL NEURAL-NETWORK; EMPIRICAL MODE DECOMPOSITION; NATURAL-GAS CONSUMPTION; SUPPORT VECTOR MACHINE; ELECTRICITY PRICE; WAVELET TRANSFORM; HYBRID MODEL; CARBON PRICE; FEATURE-SELECTION;
D O I：
10.1016/j.eneco.2019.05.006
中图分类号：
F [经济];
学科分类号：
02 ;
摘要：
Machine learning (ML) is generating new opportunities for innovative research in energy economics and finance. We critically review the burgeoning literature dedicated to Energy Economics/Finance applications of ML. Our review identifies applications in areas such as predicting energy prices (e.g. crude oil, natural gas, and power), demand forecasting, risk management, trading strategies, data processing, and analyzing macro/energy trends. We critically review the content (methods and findings) of more than 130 articles published between 2005 and 2018. Our analysis suggests that Support Vector Machine (SVM), Artificial Neural Network (ANN), and Genetic Algorithms (GAs) are among the most popular techniques used in energy economics papers. We discuss the achievements and limitations of existing literature. The survey concludes by identifying current gaps and offering some suggestions for future research. (C) 2019 Elsevier B.V. All rights reserved.
引用
收藏
页码：709 / 727
页数：19
相关论文
共 220 条
[1]  
Abdella M, 2005, ICCC 2005: IEEE 3rd International Conference on Computational Cybernetics, P207
[2]   Forecasting of peak electricity demand in Mauritius using the non-homogeneous Gompertz diffusion process [J].
Adam, N. R. Badurally ;
Elahee, M. K. ;
Dauhoo, M. Z. .
ENERGY, 2011, 36 (12) :6763-6769
[3]   Google search keywords that best predict energy price volatility [J].
Afkhami, Mohamad ;
Cormack, Lindsey ;
Ghoddusi, Hamed .
ENERGY ECONOMICS, 2017, 67 :17-27
[4]   Electricity price forecasting in deregulated markets: A review and evaluation [J].
Aggarwal, Sanjeev Kumar ;
Saini, Lalit Mohan ;
Kumar, Ashwani .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2009, 31 (01) :13-22
[5]   Wavelet neural networks: A practical guide [J].
Alexandridis, Antonios K. ;
Zapranis, Achilleas D. .
NEURAL NETWORKS, 2013, 42 :1-27
[6]   Robust ensemble learning framework for day-ahead forecasting of household based energy consumption [J].
Alobaidi, Mohammad H. ;
Chebana, Fateh ;
Meguid, Mohamed A. .
APPLIED ENERGY, 2018, 212 :997-1012
[7]   Day-ahead price forecasting of electricity markets by a new fuzzy neural network [J].
Amjady, N .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2006, 21 (02) :887-896
[8]   A new spinning reserve requirement forecast method for deregulated electricity markets [J].
Amjady, Nima ;
Keynia, Farshid .
APPLIED ENERGY, 2010, 87 (06) :1870-1879
[9]   Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting [J].
An, Ning ;
Zhao, Weigang ;
Wang, Jianzhou ;
Shang, Duo ;
Zhao, Erdong .
ENERGY, 2013, 49 :279-288
[10]   Adaptive Stochastic Control for the Smart Grid [J].
Anderson, Roger N. ;
Boulanger, Albert ;
Powell, Warren B. ;
Scott, Warren .
PROCEEDINGS OF THE IEEE, 2011, 99 (06) :1098-1115
12345678910