Electric Load Prediction at Sidoarjo Substation Using Long Short-Term Memory (LSTM) Method
Prediksi Beban Listrik Pada Gardu Induk Sidoarjo Menggunakan Metode Long Short-Term Memory (LSTM)
DOI:
https://doi.org/10.21070/2yq57j52Keywords:
electric load forecasting, sidoarjo substation, long short-term memoryAbstract
Electric Load Prediction at Sidoarjo Substasion Using Long Short-Term Memory (LSTM) Method. Load forecasting at substation support operational planning and helps anticipate overload, yet substation loads are dynamic and nonlinear. This study applies an LSTM model to forecast short-term loads at 17 measurement point (transformer incoming and distribution feeders) at the Sidoarjo Substation using one year of operational data. Preprocessing included month conversion, session encoding, chronological sorting, Min-Max normalization, and a three-step sliding window. The model uses one 50-unit LSTM layer and a Dense output layer trained with the Adam optimizer and MSE loss; January-November data were used for training and December for testing. The klurak Bali feeder achieved the lowest error (RMSE 5.50 A; MAPE 3.41%), while Heaven land showed the highest (RMSE 40.13 A; MAPE 23.64%). LSTM models stable load patterns effectively, but accuracy declines for highly fluctuating loads.
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