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
JournalUnknown Journal
Publication statusPublished - 2009

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Keywords

  • Electricity market
  • Forecasting
  • Grid computing
  • Neural networks
  • Optimization
  • Price

ASJC Scopus subject areas

  • Control and Systems Engineering

Cite this

Mohd Isa, A., Niimura, T., Sakamoto, N., Ozawa, K., & Yokoyama, R. (2009). Electricity market forecasting using artificial neural network models optimized by grid computing. Unknown Journal, 273-277.