Publicação

Dynamic artificial neural network for electricity market prices forecast

Ver documento

Detalhes bibliográficos
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).
Autores principais:Pinto, Tiago
Outros Autores:Sousa, Tiago; Vale, Zita
Assunto:Dynamic artificial neural network Electricity market prices forecast Artificial neural network Forecasting of electricity market prices
Ano:2012
País:Portugal
Tipo de documento:documento de conferência
Tipo de acesso:acesso restrito
Instituição associada:Instituto Politécnico do Porto
Idioma:inglês
Origem:Repositório Científico do Instituto Politécnico do Porto

Registos relacionados

Registos relacionados