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Lembaga Penelitian Pengabdian Masyarakat Universitas Nusa Mandiri
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PREDIKSI BEBAN LISTRIK JANGKA PENDEK BERBASIS BACKWARD ELIMINATION
Keywords:
Neural Network based, Artificial Neural Network Approach, Backward Elimination, Prediksi Beban Listrik Jangka Pendek
Abstract
Short-term electrical load prediction is one way that can be used to generate and distribute electrical energy economically so that the provider can determine the load and power demand for some time to come. Many researchers who examined the short-term electric load by the method of Neural Network. However, the use of the data set that a lot of the neural network can result in an excessive number of neurons so that can cause over generalizes phenomenon so that the necessary process of feature selection to reduce attributes in the data set that much. This research begins with the ability to process data loads daily system time series per-30 minutes. The method used is a Neural Network based on Backward Elimination with the input data used is the data in January 2012. Several experiments were conducted to obtain the optimal architecture and generate accurate predictions. The results showed an experiment with Neural Network-based methods Backward Elimination produces a lower RMSE is 0,018 compared to RMSE produced by the method of Neural Network is 0,035.
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References
Adepoju, A, G. Ogunjuyigbe, A, O, S. Alawode, O, K. (2007).Application of Neural Network to Load Forecasting in Nigerian Electrical Power System.The Pacific Journal of Science and Technology.8(1).68-72.
Alfarez, K, Hesham&Nazeeruddin, Mohammad. (2002). Electric Load Forecasting: Literature Survey and Classification Of Methods. International Journal of System Science. 1. 23-34.
Hayati, Mohsen &Shirvany, Yazdan. (2007). Artificial Neural Network Approach for Short Term Load Forecasting for Illam Region. World Academy of Science, Engineering and Technology. 28.
Liao, Warren. T. &Triantaphyllou.Evangelos. (2007). Recent Advances in Data Mining of Enterprise Data: Algorithms and Applications. Series: Computer and Operation Research. 6. 190.
Maimon, Oded&Rokach, Lior. (2010). Data Mining and Knowledge Discovery Handbook. (2nd ed). New York: Springer.
Marsudi, Djiteng. (2006). Operasi Sistem TenagaListrik. Yogyakarta: GrahaIlmu.
Myatt, Glenn J. (2007). Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining. New Jersey: John Wiley & Sons, Inc.Mitchell, T.R.,
Alfarez, K, Hesham&Nazeeruddin, Mohammad. (2002). Electric Load Forecasting: Literature Survey and Classification Of Methods. International Journal of System Science. 1. 23-34.
Hayati, Mohsen &Shirvany, Yazdan. (2007). Artificial Neural Network Approach for Short Term Load Forecasting for Illam Region. World Academy of Science, Engineering and Technology. 28.
Liao, Warren. T. &Triantaphyllou.Evangelos. (2007). Recent Advances in Data Mining of Enterprise Data: Algorithms and Applications. Series: Computer and Operation Research. 6. 190.
Maimon, Oded&Rokach, Lior. (2010). Data Mining and Knowledge Discovery Handbook. (2nd ed). New York: Springer.
Marsudi, Djiteng. (2006). Operasi Sistem TenagaListrik. Yogyakarta: GrahaIlmu.
Myatt, Glenn J. (2007). Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining. New Jersey: John Wiley & Sons, Inc.Mitchell, T.R.,
Published
2013-03-15
How to Cite
Apriana, V. (2013). PREDIKSI BEBAN LISTRIK JANGKA PENDEK BERBASIS BACKWARD ELIMINATION. Jurnal Pilar Nusa Mandiri, 9(1), 23-28. https://doi.org/10.33480/pilar.v9i1.121
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