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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)

Registered author(s):

    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.

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    Article provided by ISEG, Universidade de Lisboa in its journal Portuguese Journal of Management Studies.

    Volume (Year): XII (2007)
    Issue (Month): 2 ()
    Pages: 133-143

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    Handle: RePEc:pjm:journl:v:xii:y:2007:i:2:p:133-143
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