Dynamic artificial neural network for electricity market prices forecast


Autoria(s): Pinto, Tiago; Sousa, Tiago; Vale, Zita
Data(s)

15/04/2013

15/04/2013

2012

11/04/2013

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).

Identificador

DOI 10.1109/INES.2012.6249850

978-1-4673-2693-3

978-1-4673-2694-0

http://hdl.handle.net/10400.22/1318

Idioma(s)

eng

Publicador

IEEE

Relação

http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6249850

Direitos

closedAccess

Palavras-Chave #Dynamic artificial neural network #Electricity market prices forecast #Artificial neural network #Forecasting of electricity market prices
Tipo

conferenceObject