Short Term Forecasting of Electricity Load: A Comparison of Methods to Paiton Subsystem East Java & Bali
DOI:
https://doi.org/10.25126/jitecs.201943159Abstract
Electricity consumption in Indonesia is expected to continue to grow by average of 6,5% per year until 2020. Therefore, PT. PLN had to make an effective subsystem that can provide electrical energy based on customer needs. The electrical energy is converted from the mechanical energy and can’t be stored. Because of that reason, if the electrical energy isn’t channeled properly then PT. PLN will suffer losses. It is necessary to plan a proper distribution system of electrical energy. The aim of this research is to predict short-term electricity consumption for Paiton’s subsystem in East Java Indonesia by using ARIMA and Multilayer Perceptron. The best model is measured based on MAPE, SMAPE, and RMSE value in data sample. The result of the analysis shows that Multilayer Perceptron method provides better accuracy rate for electricity consumption forecasting in Paiton subsystem based on peak load compared to ARIMAReferences
BPPT, "The Development of The Electrical System in The Long Term National Development," Agency Assessment and Application of Technology (Jakarta), 2006.
Bunn, D. Farmer, E, "Economic and Operational Context of Electric Load 290 JITeCS Volume 4, Number 3, Desember 2019, pp 284-290p-ISSN: 2540-9433; e-ISSN: 2540-9824
Prediction, Comparative Models for Electrical Load Forecasting," pp 3-11, 1985.
Endharta, A.J., and Suhartono, "Peramalan Konsumsi Listrik Jangka Pendek dengan ARIMA Musiman Ganda dan ELMAN-Recurrent Neural Network," in Jurnal Ilmiah Teknologi Informasi, 2009.
Soares, L. J., and Medeiros, M. C, “Modelling and Forecasting Short-Term Electricity Load: A Comparison of Method with an Application to Brazilian Data,†Science Direct, pp 630-644, 2008.
Azadeh, A., Saberi, M., Nadimi, V., Iman, M., and Behrooznia, A. "An Integrated Neuro-Fuzzy Algorithm For Long-Term Electricity Consumption: Case of Selected EU Countries ," Journal of Acta Polytechnica Hungarica’, pp 71-90, 2010.
Zhang, G, "Time Series Forecasting Using a Hybrid ARIMA and Neural Network Model," in Neurocomputing, pp 159-175, 2003.
Wei, W, "Time Analysis Univariate and Multivariate Methods," in Addison Wesley Publishing Company, 2006.
Suhartono and Subanar, "A Comparative Study of Forecasting Models for Trend and Seasonal Time Series: Does Complex Model Always Yoeld Better Forecast Than Simple Models," in Jurnal Teknik Industri, 22-30, 2005.
Kocyigit, Y. A., "Classfification of EEG Recordings by Using Fast Independent Component Analysis and Artificial Neural Network," in Artificial Intelligence pp 17-20, 2008.
Faraway, J., and Chatfield, C, "Time Series forecasting with Neural Network: A Comparative Study Using The Airline Data," in Applied Statistic, pp 231-250,1998.
Downloads
Published
How to Cite
Issue
Section
License
 Creative Common Attribution-ShareAlike 3.0 International (CC BY-SA 3.0)
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
