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.
| Original language | English |
|---|---|
| Pages (from-to) | 273-277 |
| Number of pages | 5 |
| Journal | Unknown Journal |
| Publication status | Published - 2009 |
Keywords
- Electricity market
- Forecasting
- Grid computing
- Neural networks
- Optimization
- Price
ASJC Scopus subject areas
- Control and Systems Engineering
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Mohd Isa, A., Niimura, T., Sakamoto, N., Ozawa, K., & Yokoyama, R. (2009). Electricity market forecasting using artificial neural network models optimized by grid computing. Unknown Journal, 273-277.