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 |
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Keywords
- Electricity market
- Forecasting
- Grid computing
- Neural networks
- Optimization
- Price
ASJC Scopus subject areas
- Control and Systems Engineering
Cite this
Electricity market forecasting using artificial neural network models optimized by grid computing. / Mohd Isa, Aishah; Niimura, Takahide; Sakamoto, Noriaki; Ozawa, Kazuhiro; Yokoyama, Ryuichi.
In: Unknown Journal, 2009, p. 273-277.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Electricity market forecasting using artificial neural network models optimized by grid computing
AU - Mohd Isa, Aishah
AU - Niimura, Takahide
AU - Sakamoto, Noriaki
AU - Ozawa, Kazuhiro
AU - Yokoyama, Ryuichi
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
KW - Electricity market
KW - Forecasting
KW - Grid computing
KW - Neural networks
KW - Optimization
KW - Price
UR - http://www.scopus.com/inward/record.url?scp=84894585190&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84894585190&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:84894585190
SP - 273
EP - 277
JO - Nuclear Physics A
JF - Nuclear Physics A
SN - 0375-9474
ER -