Box-Jenkins method for electricity consumption forecast for 2021-2023

Authors

  • Dennis Uriel Anasco-Chata Faculty of Statistical Engineering and Computer Science-UNA-Puno Author
  • Percy Huata-Panca Faculty of Statistical Engineering and Computer Science-UNA-Puno Author
  • Ecler Mamani-Vilca Department of Informatics and Systems Engineering- UNAMBA-Apurímac Author
  • Adolfo Carlos Jimenez-Chura Professional School of Systems Engineering -UNA-Puno Author
  • Pablo Cesar Tapia-Catacora Professional School of Systems Engineering -UNA-Puno Author

Keywords:

Box-Jenkins, Forecasting, Electric Power

Abstract

The work was to determine a time series model based on the Box-Jenkins method, which is appropriately adjusted to the original series in the forecast of electricity consumption in the region of Puno Peru,  for the next three years (2021-2023), this would allow the company Electro Puno to have the model to support decision making, which conform to its corporate objectives in a medium-term time horizon, ensuring the distribution and marketing of electricity. The study was quantitative with non-experimental design, trend longitudinal cut, the population was 84 reports of monthly electricity consumption represented in (MWh/month), considering a commercial period of 7 years, the three phases of the Box-Jenkins methodology were applied as: identification, estimation and validation of the model, arriving to determine a multiplicative seasonal ARIMA (0,1,3)×(2,0,0,0)_12 model that adjusted perfectly to the series under study, being of "high precision" with a MAPE value of 2. 79% , finally in the last phase of the methodology, monthly forecasts of electricity consumption were made, with an average deviation for each forecast of a MAE value =625.80 MWh.

Downloads

Download data is not yet available.

Author Biographies

  • Dennis Uriel Anasco-Chata, Faculty of Statistical Engineering and Computer Science-UNA-Puno

    Dennis Uriel Añasco-Chata, Universidad Nacional del Altiplano Puno - Peru. Statistical and Computer Engineer, developer of statistical and computer applications.

  • Percy Huata-Panca, Faculty of Statistical Engineering and Computer Science-UNA-Puno

    Percy Huata-Panca, Universidad Nacional del Altiplano Puno - Peru, Doctoris Scientiae in Economics and Management, Doctorate in Applied Statistics, Magister Scientiae in Computer Science, Statistical Engineer, Full-time Professor at Universidad Nacional del Altiplano Puno - Peru.

  • Ecler Mamani-Vilca, Department of Informatics and Systems Engineering- UNAMBA-Apurímac

    Ecler Mamani-Vilca, Universidad Nacional Micaela Bastidas de Apurímac - Peru, Dr. in Computer Science, developer of multimedia applications and Intercultural Educational Software, full time professor at the Uni-versidad Nacional Micaela Bastidas de Apurímac.

  • Adolfo Carlos Jimenez-Chura, Professional School of Systems Engineering -UNA-Puno

    Adolfo Carlos Jimenez-Chura, Universidad Nacional del Altiplano Puno - Peru, Doctoris Scientiae in Computer Science, Magister Scientiae in Computer Science, Systems Engineer, Full-time Professor at the Universidad Nacional del Altiplano Puno - Peru.

  • Pablo Cesar Tapia-Catacora, Professional School of Systems Engineering -UNA-Puno

    Pablo Cesar Tapia-Catacora, Universidad Nacional del Altiplano Puno - Peru, Doctoris Scientiae in Computer Science, Master in Accounting and Administration, Systems Engineer, Full-time Professor at Universidad Nacional del Altiplano Puno - Peru.

References

Banco Mundial, «https://www.worldbank.org,» 09 09 2023. [En línea]. Available: https://www.worldbank.org.

B. Mundial, «https://www.worldbank.org,» 12 09 2022. [En línea]. Available: https://www.worldbank.org/en/news/feature/2018/04/18/access-energy-sustainable-development-goal-7.

M. Phillips, «https://marketrealist.com,» Market Realist, 10 10 2022. [En línea]. Available: https://marketrealist.com/2014/09/must-know-factors-impact-electricity-demand/.

Y. L. R. Quispe Pacco, «Modelo univariante para el consumo de energía eléctrica Doméstica en el Distrito de Ayaviri–Electro Puno, periodo 2004-2013,» UNA , Puno, 2015.

K. Y. Vásquez Díaz y M. E. Gamonal Sánchez , «Modelo para el pronósito del consumo mensual de energía electrica, de la provincia de Bagua Grande, mediante la metología Box Jenkis par eñ año 2016,» Universidad de Pedro Ruíz Gallo, Lambayeque, 2019.

L. G. Villarreal Escate y E. A. Marcelo Barreto, «El modelo estocástico univariante ARIMA como herramienta predictiva de la demanda de energía eléctrica residencial del sistema eléctrico Cusco,» Universidad Nacional de Ingeniería, Lima, 2021.

F. Villareal, «Introducción a los Modelos de Pronósticos,» Universidad Nacional del Sur, Bahia Blanca, 2016.

F. Parra, Estadística y Machine Learning con R, Madrid: https://bookdown.org/, 2019.

E. Court Monteverde y E. Williams Rengifo, Estadísticas y econometría financiera, Argentica: Cengage Learning Argentina, 2005.

Downloads

Published

2023-10-24

Issue

Section

Artículos