### 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 |
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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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## Cite this

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.