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Publicação
Electricity market price forecasting by grid computing optimizing artificial neural networks
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Orientador(es)
Resumo(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.
Descrição
Palavras-chave
Grid computing electricity market prices forecasting neural networks
Contexto Educativo
Citação
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