The development of machine learning in energy trading

Zhibo Ma, Chi Zhang, Chen Qian

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The energy industry has changed at unforeseeable speed. The level of renewable energy in the power system has reached record high year on year. This has brought certain challenges to the prediction and trading of the energy using conventional methods in the ever weather dependent market. In this paper, we will review some of the machine learning technologies that have been used in financial market and can be extended to energy trading. The paper will also cover the unique situation of energy market, i.e. Not economical for large scale of storage. This paper will also have a brief overview of the use of machine learning in demand forecasting.

Original languageEnglish
Title of host publication1st International Conference on Industrial Artificial Intelligence, IAI 2019
Place of PublicationU. S. A.
PublisherIEEE
ISBN (Electronic)9781728135939
DOIs
Publication statusPublished - 1 Jul 2019
Event1st International Conference on Industrial Artificial Intelligence, IAI 2019 - Shenyang, China
Duration: 22 Jul 201926 Jul 2019

Publication series

Name1st International Conference on Industrial Artificial Intelligence, IAI 2019

Conference

Conference1st International Conference on Industrial Artificial Intelligence, IAI 2019
CountryChina
CityShenyang
Period22/07/1926/07/19

Keywords

  • Deep learning
  • Demand Forecasting
  • Energy Trading
  • Machine learning

ASJC Scopus subject areas

  • Process Chemistry and Technology
  • Artificial Intelligence
  • Computer Science Applications
  • Industrial and Manufacturing Engineering
  • Control and Optimization

Cite this

Ma, Z., Zhang, C., & Qian, C. (2019). The development of machine learning in energy trading. In 1st International Conference on Industrial Artificial Intelligence, IAI 2019 [8850824] (1st International Conference on Industrial Artificial Intelligence, IAI 2019). IEEE. https://doi.org/10.1109/ICIAI.2019.8850824