Vol.12, No.3, August 2023.                                                                                                                                                                               ISSN: 2217-8309

                                                                                                                                                                                                                        eISSN: 2217-8333


TEM Journal



Association for Information Communication Technology Education and Science

Time Series Regression: Prediction of Electricity Consumption Based on Number of Consumers at National Electricity Supply Company


Mohammad Idhom, Akhmad Fauzi, Trimono Trimono, Prismahardi Riyantoko


© 2023 Trimono Trimono, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)


Citation Information: TEM Journal. Volume 12, Issue 3, Pages 1575-1581, ISSN 2217-8309, DOI: 10.18421/TEM123-39, August 2023.


Received: 28 April 2023.

Revised:   31 July 2023.
Accepted: 09 August 2023.
Published: 28 August 2023.




Electrical energy is one of the components of Gross Domestic Product that is able to encourage the economy because it has become a basic need of the community. To meet the increasing demand for electrical energy, the Indonesia National Electricity Providers (PLN) need to predict the amount of electrical power required based on the customer numbers to meet the demand for adequate electricity supply. This study aims to predict electric power based on electricity user customers using a time series regression model. The data used in this study are secondary data which get from PLN annual report in 2021. This study resulted in a finding of the best prediction model based on the Akaike Information Criterion (AIC) value, namely the time series regression model with the error value modeled by the AR(1) model, while the forecasting accuracy measure used the value MAPE of 9.77%. This means that the result of model prediction is highly accurate.


Keywords –Power electricity usage, number of consumers, time series regression, MAPE.



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