The development of machine learning in energy trading

Zhibo Ma; Chi Zhang; Chen Qian

doi:10.1109/ICIAI.2019.8850824

The development of machine learning in energy trading

Zhibo Ma, Chi Zhang, Chen Qian

Department of Electronic & Electrical Engineering

Research output: Chapter or section in a book/report/conference proceeding › Chapter in a published conference proceeding

3 Citations (SciVal)

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 language	English
Title of host publication	1st International Conference on Industrial Artificial Intelligence, IAI 2019
Place of Publication	U. S. A.
Publisher	IEEE
ISBN (Electronic)	9781728135939
DOIs	https://doi.org/10.1109/ICIAI.2019.8850824
Publication status	Published - 1 Jul 2019
Event	1st International Conference on Industrial Artificial Intelligence, IAI 2019 - Shenyang, China Duration: 22 Jul 2019 → 26 Jul 2019

Publication series

Name	1st International Conference on Industrial Artificial Intelligence, IAI 2019

Conference

Conference	1st International Conference on Industrial Artificial Intelligence, IAI 2019
Country/Territory	China
City	Shenyang
Period	22/07/19 → 26/07/19

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ICIAI.2019.8850824

Cite this

The development of machine learning in energy trading. / Ma, Zhibo; Zhang, Chi; Qian, Chen.
1st International Conference on Industrial Artificial Intelligence, IAI 2019. U. S. A.: IEEE, 2019. 8850824 (1st International Conference on Industrial Artificial Intelligence, IAI 2019).

Research output: Chapter or section in a book/report/conference proceeding › Chapter in a published conference proceeding

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, U. S. A., 1st International Conference on Industrial Artificial Intelligence, IAI 2019, Shenyang, China, 22/07/19. https://doi.org/10.1109/ICIAI.2019.8850824

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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.",

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AB - 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.

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M3 - Chapter in a published conference proceeding

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The development of machine learning in energy trading

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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