Machine learning in energy economics and finance: A review

Hamed Ghoddusi, Germán G. Creamer, Nima Rafizadeh

Research output: Contribution to journalArticlepeer-review

278 Scopus citations

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.

Original languageEnglish
Pages (from-to)709-727
Number of pages19
JournalEnergy Economics
Volume81
DOIs
StatePublished - Jun 2019

Keywords

  • Artificial Neural Network
  • Crude oil
  • Electricity price
  • Energy finance
  • Energy markets
  • Forecasting
  • Machine learning
  • Support Vector Machine

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