Short Term Forecasting of Electricity Load: A Comparison of Methods to Paiton Subsystem East Java & Bali
AbstractElectricity consumptionÂ in Indonesia is expected to continue to grow by average of 6,5% per year until 2020. Therefore, PT. PLN had toÂ make an effective subsystem that can provideÂ electrical energyÂ based on customer needs. The electrical energy isÂ converted fromÂ the mechanical energyÂ andÂ canâ€™t be stored. Because of that reason,Â ifÂ theÂ electrical energy isnâ€™t channeled properly then PT. PLN will suffer losses. It is necessary to plan a proper distribution systemÂ ofÂ electrical energy. The aim of this research is to predict short-term electricity consumption for Paitonâ€™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 dataÂ 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 comparedÂ toÂ ARIMA
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, â€œModelling and Forecasting Short-Term Electricity Load: A Comparison of Method with an Application to Brazilian Data,â€ 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 Hungaricaâ€™, 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.
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