2005
DOI: 10.1109/tpwrs.2004.840412
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Modeling and Forecasting Electricity Prices with Input/Output Hidden Markov Models

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Cited by 240 publications
(99 citation statements)
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“…Mainly for price forecasting the approaches can be classified into two categories [6] [27]- [35] 1) time series and 2) simulation approach, time series mainly relies on the historical data of market prices. In simulation approach requires precise modeling of power system equipments and their cost information, because of large amount of data involved simulation method can be computationally intensive.…”
Section: Prices Forecasting Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…Mainly for price forecasting the approaches can be classified into two categories [6] [27]- [35] 1) time series and 2) simulation approach, time series mainly relies on the historical data of market prices. In simulation approach requires precise modeling of power system equipments and their cost information, because of large amount of data involved simulation method can be computationally intensive.…”
Section: Prices Forecasting Methodsmentioning
confidence: 99%
“…Unlike load forecasting, electricity price forecasting is much more complex because of the unique characteristics and uncertainties in operation as well as bidding strategies [5]. In other commodity markets like stock market, agricultural market price forecasting is always being at the center of studies because of its importance [6]- [9].…”
Section: Introductionmentioning
confidence: 99%
“…One of the first practical uses of hidden Markov models was for speech recognition [19], but they have also been utilized in fields as diverse as protein modeling [20], economic forecasting [21], team military tactics [22], and cognitive skill acquisition [23]. …”
Section: Hidden Markov Modelmentioning
confidence: 99%
“…HMM, a powerful pattern recognizer, classifies the events in a probabilistic manner based on fault signal waveform and characteristics [15]. The HMM ability to solve small sample, nonlinear, and high-dimensional pattern problems make this algorithm a powerful choice for application in power system disturbance classifications [16,17], partial discharge de-noising [18], accidents identification and decision making in power plants [19], modeling and forecasting electrical power markets [20][21][22], and power transformer fault diagnosis based on dissolved gas analysis [23]. In [24,25], HMMs were specially applied to power transformer differential protection.…”
Section: Introductionmentioning
confidence: 99%