Forecasting electricity prices by extracting dynamic common factors: application to the Iberian Market

García‐Martos, Carolina; Rodríguez, Julio; Sánchez, Marı́a Jesús

doi:10.1049/iet-gtd.2011.0009

Cited by 37 publications

(36 citation statements)

References 28 publications

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“…Bierbrauer et al 2007;Conejo et al 2005;Garcia-Martos et al 2012;Karakatsani and Bunn 2008;Kristiansen 2012;Maciejowska and Weron 2013;Misiorek et al 2006;Weron and Misiorek 2008). However, in terms of predicting spot price movements, each model specification yields a different forecast.…”

Section: Introductionmentioning

confidence: 99%

Computing electricity spot price prediction intervals using quantile regression and forecast averaging

2014

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We examine possible accuracy gains from forecast averaging in the context of interval forecasts of electricity spot prices. First, we test whether constructing empirical prediction intervals (PI) from combined electricity spot price forecasts leads to better forecasts than those obtained from individual methods. Next, we propose a new method for constructing PI-Quantile Regression Averaging (QRA)-which utilizes the concept of quantile regression and a pool of point forecasts of individual (i.e. not combined) models. While the empirical PI from combined forecasts do not provide significant gains, the QRA-based PI are found to be more accurate than those of the best individual model-the smoothed nonparametric autoregressive model.

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Section: Introductionmentioning

confidence: 99%

Computing electricity spot price prediction intervals using quantile regression and forecast averaging

2014

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“…These factors, like the variables, evolve through time and allow one to obtain information about the larger dataset with a simpler model. The explanation here follows [12]. As there, once the common factors are obtained, univariate seasonal ARIMA models are fitted to them.…”

Section: Dynamic Factor Model (Dfm)mentioning

confidence: 99%

“…To estimate the factors F t , singular value decomposition (SVD) is used (as in [8]) for the covariance of the 24 dimensional vectors of centered prices [12]. This consists of calculating the eigenvalues, and their associated eigenvectors, for the sample covariance matrix, and thereupon calculating the matrix of common factors,F, as a linear combination of the time series:…”

Section: Dynamic Factor Model (Dfm)mentioning

confidence: 99%

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Electricity Price Forecasting by Averaging Dynamic Factor Models

2016

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Abstract:In the context of the liberalization of electricity markets, forecasting prices is essential. With this aim, research has evolved to model the particularities of electricity prices. In particular, dynamic factor models have been quite successful in the task, both in the short and long run. However, specifying a single model for the unobserved factors is difficult, and it cannot be guaranteed that such a model exists. In this paper, model averaging is employed to overcome this difficulty, with the expectation that electricity prices would be better forecast by a combination of models for the factors than by a single model. Although our procedure is applicable in other markets, it is illustrated with an application to forecasting spot prices of the Iberian Market, MIBEL (The Iberian Electricity Market). Three combinations of forecasts are successful in providing improved results for alternative forecasting horizons.

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“…Some of the current models for price and demand forecast are based on the ARMA-ARIMA methodology [3][4][5][6][7][8][9][10][11][12]. Others incorporate exponential smoothing [12][13][14] and data mining techniques [15][16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Applying Multi-Output Random Forest Models to Electricity Price Forecast

González

Mira

Ojeda

2016

Preprint

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Abstract:Predicting electricity prices is a very important issue in modern society, because the associated decision process under uncertainty requires accurate forecasts for the economic agents involved. In this paper, we apply the decision tree extension of Random Forests to the prediction of electricity prices in Spain, but with the novelty of modeling prices jointly with demand, with the purpose of achieving greater accuracy than with univariate response Random Forests, particularly in price prediction, as well as understanding the effect of the input variables (lagged values of price and demand, current production levels of available energy sources) on the joint of the two outputs. The results are very encouraging, providing significant increase in price prediction accuracy. Also, interesting methodological challenges appear as far as the appropriate choice of the relative weights of price and demand in the joint modeling is concerned and a new procedure to provide the importance variable ranking is proposed. The partykit (package of R software) library allowing for multivariate Random Forests has been used.

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Forecasting electricity prices by extracting dynamic common factors: application to the Iberian Market

Cited by 37 publications

References 28 publications

Computing electricity spot price prediction intervals using quantile regression and forecast averaging

Computing electricity spot price prediction intervals using quantile regression and forecast averaging

Electricity Price Forecasting by Averaging Dynamic Factor Models

Applying Multi-Output Random Forest Models to Electricity Price Forecast

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