Peak Load Forecasting Using Long-Short Term Memory : Case Study of Jawa-Madura-Bali System

The process demand forecast at PLN uses many assumptions of projections originating from external PLN, such as economic growth assumptions, population growth, population, inflation, electrification ratio targets, and new and renewable energy development targets. This paper provides an alternative method of calculating annual peak load forecasts using the Long Short-term Memory (LSTM) approach as a part of the Deep Neural Network in Artificial Intelligence (AI). This method aims to improve the accuracy of expense forecasts on the realization of expenses that have occurred by studying patterns that happened in the past. The calculation of the load forecast shows that the Root Mean Square Error (RMSE) of the peak load forecast with the Recurrent Neural Network (RNN)LSTM maximum is 2,167. The Mean Absolute Percentage Error (MAPE) value of the RNN-LSTM obtained a maximum of 8.6% or fell within the range <10% (very accurate category)