Semantic Scholar Open Access 2019 383 sitasi

Machine learning in energy economics and finance: A review

Hamed Ghoddusi Germán G. Creamer Nima Rafizadeh

Abstrak

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.

Topik & Kata Kunci

Computer Science

Penulis (3)

Hamed Ghoddusi

Germán G. Creamer

Nima Rafizadeh

Format Sitasi

APA MLA BibTeX

Ghoddusi, H., Creamer, G.G., Rafizadeh, N. (2019). Machine learning in energy economics and finance: A review. https://doi.org/10.1016/J.ENECO.2019.05.006

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Informasi Jurnal

Tahun Terbit: 2019
Bahasa: en
Total Sitasi: 383×
Sumber Database: Semantic Scholar
DOI: 10.1016/J.ENECO.2019.05.006
Akses: Open Access ✓