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Electricity Market Price Forecasting By Grid Computing Optimizing Artificial Neural Networks


  • T. Niimura

    (Faculty of Economics, Hosei University, Tokyo)

  • K. Ozawa

    () (Faculty of Economics, Hosei University, Tokyo)

  • N. Sakamoto

    (Faculty of Economics, Hosei University, Tokyo)


This paper presents a grid computing approach to parallel-process a neural network time-series model for forecasting electricity market prices. A grid computing environment introduced in a university computing laboratory provides access to otherwise underused computing resources. 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. Results of numerical tests using real market data on twenty grid-connected PCs are reported.

Suggested Citation

  • T. Niimura & K. Ozawa & N. Sakamoto, 2007. "Electricity Market Price Forecasting By Grid Computing Optimizing Artificial Neural Networks," Portuguese Journal of Management Studies, ISEG, Universidade de Lisboa, vol. 0(2), pages 133-143.
  • Handle: RePEc:pjm:journl:v:xii:y:2007:i:2:p:133-143

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