Day-ahead electricity price forecasting via the application of artificial neural network based models

Panapakidis, Ioannis P.; Dagoumas, Athanasios

doi:10.1016/j.apenergy.2016.03.089

Cited by 274 publications

(117 citation statements)

References 120 publications

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“…Previous work has shown that classic feedforward ANN may provide outstanding results in time series prediction tasks [7,[10][11][12][13][14]24,25]. In this study, we have selected two neural network architectures aimed specifically at this problem to be used as prediction models for energy consumption.…”

Section: Methodsmentioning

confidence: 99%

An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings

et al. 2016

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This paper addresses the problem of energy consumption prediction using neural networks over a set of public buildings. Since energy consumption in the public sector comprises a substantial share of overall consumption, the prediction of such consumption represents a decisive issue in the achievement of energy savings. In our experiments, we use the data provided by an energy consumption monitoring system in a compound of faculties and research centers at the University of Granada, and provide a methodology to predict future energy consumption using nonlinear autoregressive (NAR) and the nonlinear autoregressive neural network with exogenous inputs (NARX), respectively. Results reveal that NAR and NARX neural networks are both suitable for performing energy consumption prediction, but also that exogenous data may help to improve the accuracy of predictions.

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Section: Methodsmentioning

confidence: 99%

An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings

et al. 2016

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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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“…This is used by e.g. [36][37][38][39][40][41]. Especially in artificial intelligent based methods where a training set is provided to e.g.…”

Section: Additional Public Holiday Dummiesmentioning

confidence: 99%

Modeling public holidays in load forecasting: a German case study

Ziel

2018

J. Mod. Power Syst. Clean Energy

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We address the issue of public or bank holidays in electricity load modeling and forecasting. Special characteristics of public holidays such as their classification into fixed-date and weekday holidays are discussed in detail. We present state-of-the-art techniques to deal with public holidays such as removing them from the data set, treating them as Sunday dummy or introducing separate holiday dummies. We analyze pros and cons of these approaches and provide a large load forecasting study for Germany that compares the techniques using standard performance and significance measures. Finally, we give general recommendations for the treatment of public holidays in energy forecasting to suggest how the drawbacks particular to most of the state-of-the-art methods can be mitigated. This is especially useful, as the incorporation of holiday effects can improve the forecasting accuracy during public holidays periods by more than 80%, but even for non-holidays periods, the forecasting error can be reduced by approximately 10%.

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Day-ahead electricity price forecasting via the application of artificial neural network based models

Cited by 274 publications

References 120 publications

An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings

An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings

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

Modeling public holidays in load forecasting: a German case study

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