Machine learning in energy economics and finance: A review

By Hamed Ghoddusi, Germán G. Creamer, N. Rafizadeh. Energy Economics, 2019

398 citations recorded by the source corpus.

Abstract 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.

Subfields: Economics and Econometrics, Electrical and Electronic Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/1f4b581a63e00da5e36e9b925a5939e27aa8e374

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Citations in the corpus

Tackling Climate Change with Machine Learning (2019, background): “[770] Hamed Ghoddusi, Germán G Creamer, and Nima Rafizadeh.”