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

Content Provider	IEEE Xplore Digital Library
Author	Gonzalez, A.M. Roque, A.M.S. Garcia-Gonzalez, J.
Copyright Year	1969
Abstract	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. 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.
Sponsorship	IEEE Power Engineering Society
Starting Page	13
Ending Page	24
Page Count	12
File Size	688886
File Format	PDF
ISSN	08858950
Volume Number	20
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-02-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Predictive models Economic forecasting Electricity supply industry Power system modeling Uncertainty Power generation Humans Fuels Costs price forecasting Artificial neural networks electricity markets hidden Markov models modeling competitors' behavior
Content Type	Text
Resource Type	Article
Subject	Energy Engineering and Power Technology Electrical and Electronic Engineering

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