- -

Stability of Multiple Seasonal Holt-Winters Models Applied to Hourly Electricity Demand in Spain

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

  • Estadisticas de Uso

Stability of Multiple Seasonal Holt-Winters Models Applied to Hourly Electricity Demand in Spain

Show full item record

Trull, Ó.; García-Díaz, JC.; Troncoso, A. (2020). Stability of Multiple Seasonal Holt-Winters Models Applied to Hourly Electricity Demand in Spain. Applied Sciences. 10(7):1-16. https://doi.org/10.3390/app10072630

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/160899

Files in this item

Item Metadata

Title: Stability of Multiple Seasonal Holt-Winters Models Applied to Hourly Electricity Demand in Spain
Author: Trull, Óscar García-Díaz, J. Carlos Troncoso, Alicia
UPV Unit: Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat
Issued date:
Abstract:
[EN] Electricity management and production depend heavily on demand forecasts made. Any mismatch between the energy demanded with respect to that produced supposes enormous losses for the consumer. Transmission System ...[+]
Subjects: Time series , Forecasting , Exponential smoothing , Electricity demand
Copyrigths: Reconocimiento (by)
Source:
Applied Sciences. (eissn: 2076-3417 )
DOI: 10.3390/app10072630
Publisher:
MDPI AG
Publisher version: https://doi.org/10.3390/app10072630
Project ID:
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/
Thanks:
The authors would like to thank the Spanish Ministry of Economy and Competitiveness for the support under project TIN2017-8888209C2-1-R.
Type: Artículo

References

Hobbs, B. F., Jitprapaikulsarn, S., Konda, S., Chankong, V., Loparo, K. A., & Maratukulam, D. J. (1999). Analysis of the value for unit commitment of improved load forecasts. IEEE Transactions on Power Systems, 14(4), 1342-1348. doi:10.1109/59.801894

Weron, R. (2014). Electricity price forecasting: A review of the state-of-the-art with a look into the future. International Journal of Forecasting, 30(4), 1030-1081. doi:10.1016/j.ijforecast.2014.08.008

Lora, A. T., Santos, J. M. R., Exposito, A. G., Ramos, J. L. M., & Santos, J. C. R. (2007). Electricity Market Price Forecasting Based on Weighted Nearest Neighbors Techniques. IEEE Transactions on Power Systems, 22(3), 1294-1301. doi:10.1109/tpwrs.2007.901670 [+]
Hobbs, B. F., Jitprapaikulsarn, S., Konda, S., Chankong, V., Loparo, K. A., & Maratukulam, D. J. (1999). Analysis of the value for unit commitment of improved load forecasts. IEEE Transactions on Power Systems, 14(4), 1342-1348. doi:10.1109/59.801894

Weron, R. (2014). Electricity price forecasting: A review of the state-of-the-art with a look into the future. International Journal of Forecasting, 30(4), 1030-1081. doi:10.1016/j.ijforecast.2014.08.008

Lora, A. T., Santos, J. M. R., Exposito, A. G., Ramos, J. L. M., & Santos, J. C. R. (2007). Electricity Market Price Forecasting Based on Weighted Nearest Neighbors Techniques. IEEE Transactions on Power Systems, 22(3), 1294-1301. doi:10.1109/tpwrs.2007.901670

Chatfield, C., & Yar, M. (1988). Holt-Winters Forecasting: Some Practical Issues. The Statistician, 37(2), 129. doi:10.2307/2348687

Gardner, E. S. (2006). Exponential smoothing: The state of the art—Part II. International Journal of Forecasting, 22(4), 637-666. doi:10.1016/j.ijforecast.2006.03.005

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

Taylor, J. W. (2008). An evaluation of methods for very short-term load forecasting using minute-by-minute British data. International Journal of Forecasting, 24(4), 645-658. doi:10.1016/j.ijforecast.2008.07.007

Taylor, J. W., & Espasa, A. (2008). Energy forecasting. International Journal of Forecasting, 24(4), 561-565. doi:10.1016/j.ijforecast.2008.08.001

Trull, O., García-Díaz, J. C., & Troncoso, A. (2020). Initialization Methods for Multiple Seasonal Holt–Winters Forecasting Models. Mathematics, 8(2), 268. doi:10.3390/math8020268

Trull, Ó., García-Díaz, J., & Troncoso, A. (2019). Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter. Energies, 12(6), 1083. doi:10.3390/en12061083

López, M., Sans, C., Valero, S., & Senabre, C. (2019). Classification of Special Days in Short-Term Load Forecasting: The Spanish Case Study. Energies, 12(7), 1253. doi:10.3390/en12071253

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

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

Domínguez, E. F., & Bernat, J. X. (2007). Restructuring and generation of electrical energy in the Iberian Peninsula. Energy Policy, 35(10), 5117-5129. doi:10.1016/j.enpol.2007.04.028

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

Talavera-Llames, R., Pérez-Chacón, R., Troncoso, A., & Martínez-Álvarez, F. (2018). Big data time series forecasting based on nearest neighbours distributed computing with Spark. Knowledge-Based Systems, 161, 12-25. doi:10.1016/j.knosys.2018.07.026

Bedi, J., & Toshniwal, D. (2019). Deep learning framework to forecast electricity demand. Applied Energy, 238, 1312-1326. doi:10.1016/j.apenergy.2019.01.113

Torres, J. F., Troncoso, A., Koprinska, I., Wang, Z., & Martínez‐Álvarez, F. (2019). Big data solar power forecasting based on deep learning and multiple data sources. Expert Systems, 36(4). doi:10.1111/exsy.12394

Yang, Y., Hong, W., & Li, S. (2019). Deep ensemble learning based probabilistic load forecasting in smart grids. Energy, 189, 116324. doi:10.1016/j.energy.2019.116324

Galicia, A., Talavera-Llames, R., Troncoso, A., Koprinska, I., & Martínez-Álvarez, F. (2019). Multi-step forecasting for big data time series based on ensemble learning. Knowledge-Based Systems, 163, 830-841. doi:10.1016/j.knosys.2018.10.009

Jiang, W., Wu, X., Gong, Y., Yu, W., & Zhong, X. (2020). Holt–Winters smoothing enhanced by fruit fly optimization algorithm to forecast monthly electricity consumption. Energy, 193, 116779. doi:10.1016/j.energy.2019.116779

Holt, C. C. (2004). Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting, 20(1), 5-10. doi:10.1016/j.ijforecast.2003.09.015

Makridakis, S., & Hibon, M. (1991). Exponential smoothing: The effect of initial values and loss functions on post-sample forecasting accuracy. International Journal of Forecasting, 7(3), 317-330. doi:10.1016/0169-2070(91)90005-g

Archibald, B. C. (1990). Parameter space of the Holt-winters’ model. International Journal of Forecasting, 6(2), 199-209. doi:10.1016/0169-2070(90)90005-v

Lawton, R. (1998). How should additive Holt–Winters estimates be corrected? International Journal of Forecasting, 14(3), 393-403. doi:10.1016/s0169-2070(98)00040-5

Hyndman, R. J., Akram, M., & Archibald, B. C. (2007). The admissible parameter space for exponential smoothing models. Annals of the Institute of Statistical Mathematics, 60(2), 407-426. doi:10.1007/s10463-006-0109-x

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

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

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

Nelder, J. A., & Mead, R. (1965). A Simplex Method for Function Minimization. The Computer Journal, 7(4), 308-313. doi:10.1093/comjnl/7.4.308

Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679-688. doi:10.1016/j.ijforecast.2006.03.001

Tofallis, C. (2015). A better measure of relative prediction accuracy for model selection and model estimation. Journal of the Operational Research Society, 66(8), 1352-1362. doi:10.1057/jors.2014.103

[-]

recommendations

 

This item appears in the following Collection(s)

Show full item record