Short-term electricity price forecast based on the improved hybrid model

Dong, Yao; Wang, Jianzhou; Jiang, He; Wu, Jie

doi:10.1016/j.enconman.2011.04.020

Cited by 78 publications

(36 citation statements)

References 21 publications

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“…Out-sample data set: all the hours of the weeks with numbers 5,10,15,20,25,30,35,40,45,50 in 2012, and weeks number 2, 7, 12, 17, 22, 27, 32, 37, 42, 47 in 2013; a total of 3360 cases (h).…”

Section: Data Characteristicsmentioning

confidence: 99%

Short-Term Price Forecasting Models Based on Artificial Neural Networks for Intraday Sessions in the Iberian Electricity Market

et al. 2016

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This paper presents novel intraday session models for price forecasts (ISMPF models) for hourly price forecasting in the six intraday sessions of the Iberian electricity market (MIBEL) and the analysis of mean absolute percentage errors (MAPEs) obtained with suitable combinations of their input variables in order to find the best ISMPF models. Comparisons of errors from different ISMPF models identified the most important variables for forecasting purposes. Similar analyses were applied to determine the best daily session models for price forecasts (DSMPF models) for the day-ahead price forecasting in the daily session of the MIBEL, considering as input variables extensive hourly time series records of recent prices, power demands and power generations in the previous day, forecasts of demand, wind power generation and weather for the day-ahead, and chronological variables. ISMPF models include the input variables of DSMPF models as well as the daily session prices and prices of preceding intraday sessions. The best ISMPF models achieved lower MAPEs for most of the intraday sessions compared to the error of the best DSMPF model; furthermore, such DSMPF error was very close to the lowest limit error for the daily session. The best ISMPF models can be useful for MIBEL agents of the electricity intraday market and the electric energy industry.

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Section: Data Characteristicsmentioning

confidence: 99%

Short-Term Price Forecasting Models Based on Artificial Neural Networks for Intraday Sessions in the Iberian Electricity Market

et al. 2016

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“…Results demonstrated that the proposed model improves the forecasting accuracy noticeably compared with the existing models [17]. Dong et al [18] proposed a forecasting model that detached high volatility and daily seasonality for electricity price based on EMD. The comparisons demonstrate that the proposed model can improve the prediction accuracy noticeably [18].…”

Section: Introductionmentioning

confidence: 99%

“…Dong et al [18] proposed a forecasting model that detached high volatility and daily seasonality for electricity price based on EMD. The comparisons demonstrate that the proposed model can improve the prediction accuracy noticeably [18]. However, we have not identified the researches exploiting the multiscale analysis capability of the EMD algorithm in the multivariate portfolio risk measurement.…”

Section: Introductionmentioning

confidence: 99%

Estimating Portfolio Value at Risk in the Electricity Markets Using an Entropy Optimized BEMD Approach

Zou

2015

Entropy

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In this paper, we propose a new entropy-optimized bivariate empirical mode decomposition (BEMD)-based model for estimating portfolio value at risk (PVaR). It reveals and analyzes different components of the price fluctuation. These components are decomposed and distinguished by their different behavioral patterns and fluctuation range, by the BEMD model. The entropy theory has been introduced for the identification of the model parameters during the modeling process. The decomposed bivariate data components are calculated with the DCC-GARCH models. Empirical studies suggest that the proposed model outperforms the benchmark multivariate exponential weighted moving average (MEWMA) and DCC-GARCH model, in terms of conventional out-of-sample performance evaluation criteria for the model accuracy.

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“…In the ARIMA model, the moving average order q is decided by ACF, while PACF can determine the autoregressive order p [7].…”

Section: Parameter Estimation and Diagnostic Checkingmentioning

confidence: 99%

Comparison of ARIMA and ANN Models Used in Electricity Price Forecasting for Power Market

Gao¹,

Lo²,

Fan³

2017

EPE

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In power market, electricity price forecasting provides significant information which can help the electricity market participants to prepare corresponding bidding strategies to maximize their profits. This paper introduces the models of autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) which are applied to the price forecasts for up to 3 steps 8 weeks ahead in the UK electricity market. The half hourly data of historical prices are obtained from UK Reference Price Data from March 22 nd to July

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Short-term electricity price forecast based on the improved hybrid model

Cited by 78 publications

References 21 publications

Short-Term Price Forecasting Models Based on Artificial Neural Networks for Intraday Sessions in the Iberian Electricity Market

Short-Term Price Forecasting Models Based on Artificial Neural Networks for Intraday Sessions in the Iberian Electricity Market

Estimating Portfolio Value at Risk in the Electricity Markets Using an Entropy Optimized BEMD Approach

Comparison of ARIMA and ANN Models Used in Electricity Price Forecasting for Power Market

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