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Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter

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Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter

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Trull, Ó.; García-Díaz, JC.; Troncoso, A. (2019). Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter. Energies. 12(6):1-16. https://doi.org/10.3390/en12061083

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Título: Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter
Autor: Trull, Óscar García-Díaz, J. Carlos Troncoso, Alicia
Entidad UPV: Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat
Fecha difusión:
Resumen:
[EN] Forecasting electricity demand through time series is a tool used by transmission system operators to establish future operating conditions. The accuracy of these forecasts is essential for the precise development of ...[+]
Palabras clave: Time series , Forecasting , Exponential smoothing , Electricity demand
Derechos de uso: Reconocimiento (by)
Fuente:
Energies. (eissn: 1996-1073 )
DOI: 10.3390/en12061083
Editorial:
MDPI AG
Versión del editor: https://doi.org/10.3390/en12061083
Código del Proyecto:
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-88209-C2-1-R/ES/BIG DATA STREAMING: ANALISIS DE DATOS MASIVOS CONTINUOS. MODELOS PREDICTIVOS/
Agradecimientos:
The authors would like to thank the Spanish Ministry of Economy and Competitiveness for the support under project TIN2017-8888209C2-1-R.
Tipo: Artículo

References

Garrués-Irurzun, J., & López-García, S. (2009). Red Eléctrica de España S.A.: Instrument of regulation and liberalization of the Spanish electricity market (1944–2004). Renewable and Sustainable Energy Reviews, 13(8), 2061-2069. doi:10.1016/j.rser.2009.01.028

Roldan-Fernandez, J., Gómez-Quiles, C., Merre, A., Burgos-Payán, M., & Riquelme-Santos, J. (2018). Cross-Border Energy Exchange and Renewable Premiums: The Case of the Iberian System. Energies, 11(12), 3277. doi:10.3390/en11123277

Contreras, J., Espinola, R., Nogales, F. J., & Conejo, A. J. (2003). ARIMA models to predict next-day electricity prices. IEEE Transactions on Power Systems, 18(3), 1014-1020. doi:10.1109/tpwrs.2002.804943 [+]
Garrués-Irurzun, J., & López-García, S. (2009). Red Eléctrica de España S.A.: Instrument of regulation and liberalization of the Spanish electricity market (1944–2004). Renewable and Sustainable Energy Reviews, 13(8), 2061-2069. doi:10.1016/j.rser.2009.01.028

Roldan-Fernandez, J., Gómez-Quiles, C., Merre, A., Burgos-Payán, M., & Riquelme-Santos, J. (2018). Cross-Border Energy Exchange and Renewable Premiums: The Case of the Iberian System. Energies, 11(12), 3277. doi:10.3390/en11123277

Contreras, J., Espinola, R., Nogales, F. J., & Conejo, A. J. (2003). ARIMA models to predict next-day electricity prices. IEEE Transactions on Power Systems, 18(3), 1014-1020. doi:10.1109/tpwrs.2002.804943

Juberias, G., Yunta, R., Garcia Moreno, J., & Mendivil, C. (1999). A new ARIMA model for hourly load forecasting. 1999 IEEE Transmission and Distribution Conference (Cat. No. 99CH36333). doi:10.1109/tdc.1999.755371

Bianco, V., Manca, O., & Nardini, S. (2009). Electricity consumption forecasting in Italy using linear regression models. Energy, 34(9), 1413-1421. doi:10.1016/j.energy.2009.06.034

Taylor, J. W. (2003). Short-term electricity demand forecasting using double seasonal exponential smoothing. Journal of the Operational Research Society, 54(8), 799-805. doi:10.1057/palgrave.jors.2601589

Taylor, J. W. (2010). Triple seasonal methods for short-term electricity demand forecasting. European Journal of Operational Research, 204(1), 139-152. doi:10.1016/j.ejor.2009.10.003

Ko, C.-N., & Lee, C.-M. (2013). Short-term load forecasting using SVR (support vector regression)-based radial basis function neural network with dual extended Kalman filter. Energy, 49, 413-422. doi:10.1016/j.energy.2012.11.015

Rana, M., & Koprinska, I. (2016). Forecasting electricity load with advanced wavelet neural networks. Neurocomputing, 182, 118-132. doi:10.1016/j.neucom.2015.12.004

Baliyan, A., Gaurav, K., & Mishra, S. K. (2015). A Review of Short Term Load Forecasting using Artificial Neural Network Models. Procedia Computer Science, 48, 121-125. doi:10.1016/j.procs.2015.04.160

Yang, Z., Ce, L., & Lian, L. (2017). Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods. Applied Energy, 190, 291-305. doi:10.1016/j.apenergy.2016.12.130

Ghadimi, N., Akbarimajd, A., Shayeghi, H., & Abedinia, O. (2018). Two stage forecast engine with feature selection technique and improved meta-heuristic algorithm for electricity load forecasting. Energy, 161, 130-142. doi:10.1016/j.energy.2018.07.088

Troncoso Lora, A., Riquelme Santos, J. M., Riquelme, J. C., Gómez Expósito, A., & Martínez Ramos, J. L. (2004). Time-Series Prediction: Application to the Short-Term Electric Energy Demand. Lecture Notes in Computer Science, 577-586. doi:10.1007/978-3-540-25945-9_57

Martinez Alvarez, F., Troncoso, A., Riquelme, J. C., & Aguilar Ruiz, J. S. (2011). Energy Time Series Forecasting Based on Pattern Sequence Similarity. IEEE Transactions on Knowledge and Data Engineering, 23(8), 1230-1243. doi:10.1109/tkde.2010.227

Cancelo, J. R., Espasa, A., & Grafe, R. (2008). Forecasting the electricity load from one day to one week ahead for the Spanish system operator. International Journal of Forecasting, 24(4), 588-602. doi:10.1016/j.ijforecast.2008.07.005

TORRÓ, H., MENEU, V., & VALOR, E. (2003). Single Factor Stochastic Models with Seasonality Applied to Underlying Weather Derivatives Variables. The Journal of Risk Finance, 4(4), 6-17. doi:10.1108/eb022969

Darbellay, G. A., & Slama, M. (2000). Forecasting the short-term demand for electricity. International Journal of Forecasting, 16(1), 71-83. doi:10.1016/s0169-2070(99)00045-x

Moral-Carcedo, J., & Vicéns-Otero, J. (2005). Modelling the non-linear response of Spanish electricity demand to temperature variations. Energy Economics, 27(3), 477-494. doi:10.1016/j.eneco.2005.01.003

Erişen, E., Iyigun, C., & Tanrısever, F. (2017). Short-term electricity load forecasting with special days: an analysis on parametric and non-parametric methods. Annals of Operations Research. doi:10.1007/s10479-017-2726-6

Arora, S., & Taylor, J. W. (2013). Short-Term Forecasting of Anomalous Load Using Rule-Based Triple Seasonal Methods. IEEE Transactions on Power Systems, 28(3), 3235-3242. doi:10.1109/tpwrs.2013.2252929

Arora, S., & Taylor, J. W. (2018). Rule-based autoregressive moving average models for forecasting load on special days: A case study for France. European Journal of Operational Research, 266(1), 259-268. doi:10.1016/j.ejor.2017.08.056

Bermúdez, J. D. (2013). Exponential smoothing with covariates applied to electricity demand forecast. European J. of Industrial Engineering, 7(3), 333. doi:10.1504/ejie.2013.054134

Göb, R., Lurz, K., & Pievatolo, A. (2013). Electrical load forecasting by exponential smoothing with covariates. Applied Stochastic Models in Business and Industry, 29(6), 629-645. doi:10.1002/asmb.2008

Chatfield, C. (1978). The Holt-Winters Forecasting Procedure. Applied Statistics, 27(3), 264. doi:10.2307/2347162

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