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