The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Modeling and Forecasting Electricity Prices with Input/Output Hidden Markov Models
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.
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Cites
A tutorial on hidden Markov models and selected applications in speech recognition
Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728
Neural Networks for Pattern Recognition
Bishop, C. M. (1995) · doi:10.1093/oso/9780198538493.001.0001 · Cited by 12,287
Cited by
Electricity price forecasting: A review of the state-of-the-art with a look into the future
Weron, R. (2014) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2014.08.008 · Cited by 1,646
Day-Ahead Price Forecasting of Electricity Markets by a New Fuzzy Neural Network
Amjady, N. (2006) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2006.873409 · Cited by 371
Gollou, A. R., & Ghadimi, N. (2017) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-152073 · Cited by 267
Metadata
- Authors
- MateoGonzalez, A., MunozSanRoque, A., & Garcia-Gonzalez, J.
- Venue
- IEEE Transactions on Power Systems
- Published
- 2005-02
- Volume
- 20
- Issue
- 1
- Pages
- 13-24
- ISSN
- 0885-8950 (ISSN-L)
- OpenAlex
- W2153136237
- Cited by
- 306
- References
- 3
- FWCI
- 21.61
- Type
- journal article
- Language
- en
- Open access
- closed
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