CMAC-based short-term electricity price forecasting

Hao Zhou, Jianhua Chen, Hao Wu, Siu Lau Ho

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)

Abstract

Electricity price forecasting is, naturally, the basis of decision-making in electricity markets. This paper proposes the day-ahead short-term electricity price forecasting models using a Cerebella Model Articulation Controller (CMAC) neural network, and then constructs the respective models at different trading intervals. The data of California electricity market is employed to predict the short-term electricity price using the proposed CMAC and a BP (back-propagation) neural network. The performance comparison shows that the proposed CMAC neural network can work more steadily and speedily in short-term electricity price forecasting when compared with a BP network.
Original languageEnglish
Title of host publicationSixth International Conference on Advances in Power System Control, Operation and Management - Proceedings
Pages348-353
Number of pages6
Volume1
Publication statusPublished - 1 Dec 2003
EventSixth International Conference on Advances in Power System Control, Operation and Management - Proceedings - Hong Kong, Hong Kong
Duration: 11 Nov 200314 Nov 2003

Conference

ConferenceSixth International Conference on Advances in Power System Control, Operation and Management - Proceedings
Country/TerritoryHong Kong
CityHong Kong
Period11/11/0314/11/03

ASJC Scopus subject areas

  • Engineering(all)

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