PREDIKSI BEBAN LISTRIK JANGKA PENDEK BERBASIS BACKWARD ELIMINATION
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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