Prediksi TinggiPermukaanAir Waduk Menggunakan Artificial Neural NetworkBerbasis Sliding Window

Prediksi TinggiPermukaanAir Waduk Menggunakan Artificial Neural NetworkBerbasis Sliding Window

artificial neural network, sliding window, backpropagation, network architecture, mse

The water level in the reservoir is an important factor in the operation of a hydroelectric turbine to control water overflowso that there is no excessive degradation. This water control has an influence on the performance and production of hydroelectric energy. The daily reservoir water level (tpaw) recording of PLTA Riam Kanan is carried out through a daily direct measurement and observation process on thereservoir measuring board which is recapitulated every month in excel form. This time series historical data continues to grow every day to become a data warehouse that is still useless if only stored.Extracting knowledge from the data warehouse can be done using one of the artificial neural network data mining techniques, namely backpropagation to predict the next day's tpaw. Historical data for the tpaw time series is presented with a sliding window concept approach based on the window sizes used, namely 7, 14, 21 and 28. The window size represents the number of days as an input layer variable in the backpropagation network architecture to predict the next day's tpaw. Some backpropagation network testing is carried out using a combination of the number of window sizes against the comparison of the amount of training data and test data on the network. The prediction results obtained with the smallest mean squared error (mse) in network testing is 0.000577 as a high accuracy value of the prediction results.The network architecture with the smallest mse using 28 input layers, 10 hidden layers and 1 output layer can be a knowledge that can help the hydropower plantas an alternative in making turbine operation decisions based on the predicted results of reservoir water level.

Dwi Kartini1, Friska Abadi2, Triando Hamonangan Saragih3

https://jurnal.iaii.or.id/index.php/RESTI/issue/view/20

Universitas Lambung Mangkurat

13 Februari 2021

Fajar bagus W

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Indonesia

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