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Modeling and forecasting electricity prices with input/output hidden Markov models

A. Mateo, A. Muñoz, J. García-González

In competitive electricity markets, in addition to the uncertainty of exogenous variables such as energy demand, water inflows, and availability of generation units and fuel costs, participants are faced with the uncertainty of their competitors’ behavior. The analysis of electricity price time series reflects a switching nature, related to discrete changes in competitors’ strategies, which can be represented by a set of dynamic models sequenced together by a Markov chain. In this paper, an Input–Output Hidden Markov Model (IOHMM) is proposed for analyzing and forecasting electricity spot prices. The model provides both good predictions in terms of accuracy as well as dynamic information about the market. In this way, different market states are identified and characterized by their more relevant explanatory variables. Moreover, a conditional probability transition matrix governs the probabilities of remaining in the same state, or changing to another, whenever a new market session is opened. The model has been successfully applied to real clearing prices in the Spanish electricity market.


Keywords: Artificial neural networks, electricity markets, hidden Markov models, modeling competitors’ behavior, price forecasting.


IEEE Transactions on Power Systems. Volume: 20 Issue: 1 Pages: 13-24

Journal Impact Factor: JCR impact factor 5.255 (2017)

DOI reference: DOI icon 10.1109/TPWRS.2004.840412    

Published on paper: February 2005.


    Topics research:
  • *Forecasting and data mining

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