Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
721272 | IFAC Proceedings Volumes | 2009 | 5 Pages |
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.
Related Topics
Physical Sciences and Engineering
Engineering
Computational Mechanics
Authors
Aishah Mohd Isa, Takahide Niimura, Noriaki Sakamoto, Kazuhiro Ozawa, Ryuichi Yokoyama,