Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.22/1318
Título: Dynamic artificial neural network for electricity market prices forecast
Autor: Pinto, Tiago
Sousa, Tiago
Vale, Zita
Palavras-chave: Dynamic artificial neural network
Electricity market prices forecast
Artificial neural network
Forecasting of electricity market prices
Data: 2012
Editora: IEEE
Resumo: This paper presents an artificial neural network applied to the forecasting of electricity market prices, with the special feature of being dynamic. The dynamism is verified at two different levels. The first level is characterized as a re-training of the network in every iteration, so that the artificial neural network can able to consider the most recent data at all times, and constantly adapt itself to the most recent happenings. The second level considers the adaptation of the neural network’s execution time depending on the circumstances of its use. The execution time adaptation is performed through the automatic adjustment of the amount of data considered for training the network. This is an advantageous and indispensable feature for this neural network’s integration in ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to the market negotiating players of MASCEM (Multi-Agent Simulator of Competitive Electricity Markets).
URI: http://hdl.handle.net/10400.22/1318
ISBN: 978-1-4673-2693-3
Versão do Editor: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6249850
Aparece nas colecções:ISEP – GECAD – Comunicações em eventos científicos

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