Mixed Models for Short-Run Forecasting of Electricity Prices: Application for the Spanish Market

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

doi:10.1109/tpwrs.2007.894857

Cited by 112 publications

(66 citation statements)

References 14 publications

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“…In the context of electricity prices, García-Martos et al (2007) similarly advocate combining, but within a single model class (ARIMA), to deal with specification uncertainty. Despite the volume of comparisons published, it is an open question how many of the results in favour of combining are actually statistically significant.…”

Section: Introductionmentioning

confidence: 99%

Combining day-ahead forecasts for British electricity prices

et al. 2013

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This paper considers how well the approach of combining forecasts extends to the context of electricity prices. With the increasing popularity of regime switching and time-varying parameter models for predicting power prices, the multi model and evolutionary considerations that usually support the combining of simpler time series methods may be less applicable when the individual models incorporate these features. We address this question with a backtesting analysis on British day-ahead prices. Furthermore, given the volatility of power prices and concerns about accurate forecasting under extreme price excursions, we evaluate the results using various error metrics including expected shortfall. The comparisons are furthermore carefully simulated to consider model selection uncertainty in order to realistically test the value of combining as an ex ante policy. Overall, our results support combining for both accurate operational planning and risk management.

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Section: Introductionmentioning

confidence: 99%

Combining day-ahead forecasts for British electricity prices

et al. 2013

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“…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

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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.

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“…It is also left for future work to incorporate in the forecast combination other forecasting methods (not necessarily involving DFM); for example, the predictions obtained by the mixed model in [4], which presents extremely accurate short-term predictions for the Iberian market. With weights evolving for different time-horizons, including this model for short-term predictions could improve the results.…”

Section: Conclusion and Further Lines Of Researchmentioning

confidence: 99%

“…. , 24 at day t. Consequently, prices can be presented in a T × 24 dimensional matrix, where T is the number of days in the sample, and modeling should be multivariate (as in [2][3][4][5]). …”

Section: Introductionmentioning

confidence: 99%

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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Mixed Models for Short-Run Forecasting of Electricity Prices: Application for the Spanish Market

Cited by 112 publications

References 14 publications

Combining day-ahead forecasts for British electricity prices

Combining day-ahead forecasts for British electricity prices

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

Electricity Price Forecasting by Averaging Dynamic Factor Models

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