Electricity market price forecasting by grid computing optimizing artificial neural networks

Niimura, T.; Ozawa, K.; Sakamoto, N.

http://hdl.handle.net/10400.5/10011

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Authors

Niimura, T.

Ozawa, K.

Sakamoto, N.

Abstract(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.

Keywords

Grid computing electricity market prices forecasting neural networks

URI

http://hdl.handle.net/10400.5/10011

Citation

Niimura, T., K. Ozawa e N. Sakamoto (2007). "Electricity market price forecasting by grid computing optimizing artificial neural networks". Portuguese Journal of Management Studies, XII(2):133-144