ANALISIS ALGORITMA PREDIKSI UNTUK MENGHASILKAN PREDIKSI BEBAN LISTRIK JANGKA PENDEK
Electricity is the lifeblood for human life, both for household and industrial world. In the power supply industry, it is important to determine the power requirements for the future as soon as possible (at the earliest). Short-term electric load prediction is one way that can be used to generate and distribute electrical energy economically so that the power provider can know the load and demand for power for the next month, previous short-term power prediction studies, generally using the Neural method Network. Neural Network is an information processing system that has characteristics similar to biological neural networks but, deficiencies in Neural Network often overfitting due to overtraining. In a short-term electrical load prediction study, using the Support Vector Machine (SVM) method, Support Vector Machines (SVM) is a technique for predicting both classification and regression. This research begins by processing daily load system load data with a time span of 30 minutes, with data input used is data in January 2017. The results show that the SVM method can be one of the reference methods for the prediction of short-term electrical load with RMSE 0.034.
Apriana, V., & Handayani, R. I. (2017). Laporan Kemajuan PDP 2017-Analisis Algoritma Prediksi Untuk Menghasilkan Prediksi Beban Listrik Jangka Pendek. Jakarta: AMIK BSI Jakarta
Hayati, Mohsen &Shirvany, Yazdan. (2007). Artficial 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.
Marsudi, Djiteng. (2006). Operasi Sistem Tenaga Listrik. Yogyakarta: GrahaIlmu.
Myatt, Glenn J. (3rd ed). (2007). Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining. New Jersey: John Wiley & Sons, Inc.Mitchell, T.R., & Larson, J.R. (1987). People in organizations: An Introduction to organizational behavior. New York: McGraw-Hill.
Santosa, Budi. (2007). Data mining Teknik Pemanfaatan Data untuk Keperluan Bisnis. Yogyakarta: GrahaIlmu.
Witten, H, Ian & Frank, Eibe. (2nded). (2005). Data Mining Practical Machine Learning Tools and Techniques. .San Francisco: Morgan Kaufmann.
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