Electricity Market Price Forecasting By Grid Computing Optimizing Artificial Neural Networks
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.
Volume (Year): XII (2007)
Issue (Month): 2 ()
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