Electric Load Forecasting Using Artificial Neural Network Method with Limited Data

  • Elang Bayu Trikora Universitas Gadjah Mada
  • Sasongko Pramonohadi Universitas Gadjah Mada
  • M. Isnaeni Bambang Setyonegoro Universitas Gadjah Mada
Keywords: Load Forecasting, Artificial Neural Network, Limited Data, Interpolation


As time changes, electric load demand forecasting is one of the vital things in generation and distribution planning. Various ways can be implemented in forecasting electrical energy demands, one of which is by using the artificial neural network method, which is a method that mimics the ability of the human brain to receive an input and then carry out processing between the neurons within to produce information based on the processes that occur within the neurons. This research uses a neural network method to forecast the electric load in Jayawijaya Regency. This research builds a neural network architecture suitable to the data obtained from National Electricity Company (Perusahaan Listrik Negara, PT PLN Indonesia) UP 3 Wamena to find an architecture model suitable with high accuracy. Due to the limited data owned to forecast electric load, an interpolation method based on the owned original data was carried out to increase the amount of the existing data. In this way, more data can be used as input, allowing the model to forecast load requirements more accurately. These propagated data were used as input data in the artificial neural network model. After conducting iterative testing using a neural network, it is found that the model that fitted the data was feed-forward long short-term memory (LSTM) network, this model can obtain errors in accordance with the standards of a model to perform forecasts of 0.04% with nine epochs.


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How to Cite
Elang Bayu Trikora, Sasongko Pramonohadi, & M. Isnaeni Bambang Setyonegoro. (2023). Electric Load Forecasting Using Artificial Neural Network Method with Limited Data. Jurnal Nasional Teknik Elektro Dan Teknologi Informasi, 12(3), 227-232. https://doi.org/10.22146/jnteti.v12i3.6437