Electricity market forecasting using artificial neural network models optimized by grid computing

Aishah Mohd Isa, Takahide Niimura, Noriaki Sakamoto, Kazuhiro Ozawa, Ryuichi Yokoyama

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

This paper reports a grid computing approach to parallel-process a neural network time-series model for forecasting electricity market prices. The grid computing of the neural network model not only processes several times faster than a single iterative process but also provides chances of improving forecasting accuracy. A grid-computing environment implemented in a university computing laboratory improves utilization rate of otherwise underused computing resources. Results of numerical tests using the real market data by more than twenty grid-connected PCs are presented.

Original languageEnglish
Pages (from-to)273-277
Number of pages5
JournalIFAC Proceedings Volumes (IFAC-PapersOnline)
Publication statusPublished - 01 Dec 2009

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering

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