Application of Public-Domain Market Information to Forecast Ontario's Wholesale Electricity Prices

Zareipour, Hamidreza; Cañizares, Claudio A.; Bhattacharya, Kankar; Thomson, John Douglas

doi:10.1109/tpwrs.2006.883688

Cited by 129 publications

(57 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Solution methods of economic dispatch (ED) problems in the references [7]- [10], it generally ignores losses, so that the problems become very simple as expressed in the equation (1). 0 :…”

Section: Economic Dispatch Direct Methodsmentioning

confidence: 99%

Implementation of Electricity Competition Framework with Economic Dispatch Direct Method

Zein¹,

Sabri

2012

TELKOMNIKA

View full text Add to dashboard Cite

Abstrak Kata kunci: kerangka kompetisi kelistrikan, implementasi, perhitungan-perhitungan listrik Abstract Technically, electricity business under competition structure is more complex than that of vertically integrated one. A competition framework has to be designed to implement bidding strategies with electrical calculations as quickly as possible (not more than 15 minets). This paper proposes a competition

show abstract

“…Solution methods of economic dispatch (ED) problems in the references [7]- [10], it generally ignores losses, so that the problems become very simple as expressed in the equation (1). 0 :…”

Section: Economic Dispatch Direct Methodsmentioning

confidence: 99%

Implementation of Electricity Competition Framework with Economic Dispatch Direct Method

Zein¹,

Sabri

2012

TELKOMNIKA

View full text Add to dashboard Cite

show abstract

“…In this way, the ARIMA (autoregressive integrated moving average) models are the most representative, with different particularizations. Thus, there are references that accommodate the seasonality using the same set of parameters for all hours of the day [10,11]; and others that perform ARIMA model fitting (or its variants, AR or ARMA) for each time slot of the day [12,13]. Other generalizations of the ARIMA models are the so-called linear transfer function or transfer function models with ARIMA noise [14,15], which have the peculiarity of including past and present influence of other series.…”

Section: Previous Workmentioning

confidence: 99%

Medium-Term Probabilistic Forecasting of Extremely Low Prices in Electricity Markets: Application to the Spanish Case

2016

View full text Add to dashboard Cite

Abstract:One of the most relevant challenges that have arisen in electricity markets during the last few years is the emergence of extremely low prices. Trying to predict these events is crucial for market agents in a competitive environment. This paper proposes a novel methodology to simultaneously accomplish punctual and probabilistic hourly predictions about the appearance of extremely low electricity prices in a medium-term scope. The proposed approach for making real ex ante forecasts consists of a nested compounding of different forecasting techniques, which incorporate Monte Carlo simulation, combined with spatial interpolation techniques. The procedure is based on the statistical identification of the process key drivers. Logistic regression for rare events, decision trees, multilayer perceptrons and a hybrid approach, which combines a market equilibrium model with logistic regression, are used. Moreover, this paper assesses whether periodic models in which parameters switch according to the day of the week can be even more accurate. The proposed techniques are compared to a Markov regime switching model and several naive methods. The proposed methodology empirically demonstrates its effectiveness by achieving promising results on a real case study based on the Spanish electricity market. This approach can provide valuable information for market agents when they face decision making and risk-management processes. Our findings support the additional benefit of using a hybrid approach for deriving more accurate predictions.

show abstract

“…To facilitate comparability of the proposed model to previous work, the test sets of this section follow the same dates as tested in [3,50]. Each training set consisted of 48 days (1152 hourly measurements) followed by an out-of-sample test set consisting of 2 weeks of price data (336 hourly measurements) 1 .…”

Section: Ontario Electricity Market Price Forecastingmentioning

confidence: 99%

Nonlinear maximum likelihood estimation of electricity spot prices using recurrent neural networks

Mirikitani

Nikolaev

2010

Neural Comput & Applic

View full text Add to dashboard Cite

Electricity spot prices are complex processes characterized by nonlinearity and extreme volatility. Previous work on nonlinear modeling of electricity spot prices has shown encouraging results, and we build on this area by proposing an Expectation Maximization algorithm for maximum likelihood estimation of recurrent neural networks utilizing the Kalman filter and smoother. This involves inference of both parameters and hyper-parameters of the model which takes into account the model uncertainty and noise in the data. The Expectation Maximization algorithm uses a forward filtering and backward smoothing (Expectation) step, followed by a hyperparameter estimation (Maximization) step. The model is validated across two data sets of different power exchanges. It is found that after learning a posteriori hyper-parameters, the proposed algorithm outperforms the real-time recurrent learning and the extended Kalman Filtering algorithm for recurrent networks, as well as other contemporary models that have been previously applied to the modeling of electricity spot prices.

show abstract

Application of Public-Domain Market Information to Forecast Ontario's Wholesale Electricity Prices

Cited by 129 publications

References 18 publications

Implementation of Electricity Competition Framework with Economic Dispatch Direct Method

Implementation of Electricity Competition Framework with Economic Dispatch Direct Method

Medium-Term Probabilistic Forecasting of Extremely Low Prices in Electricity Markets: Application to the Spanish Case

Nonlinear maximum likelihood estimation of electricity spot prices using recurrent neural networks

Contact Info

Product

Resources

About