The use of electricity under the social tariff category has increased significantly each year, both purely social and commercial social use. The use of social tariff electricity is intended for public interest activities for both the lower and upper middle social strata which are oriented towards fulfilling growth and development facilities for the public interest, so that a simulation of social electricity usage is needed to map a picture of the condition of the amount of social electricity usage in the future. The research was conducted to determine the estimation of how much electricity is used by using the Elman Recurrent Neural Network (ERNN) algorithm by reducing the input dimensions. The ERNN algorithm is used to simulate network parameters formed from complex input-output relationships, so that data patterns can be found. The factors of the input dimensions of this study are demographic data, electricity usage, social customers, population, gross regional domestic product (GRDP) and industrial growth. The results showed that the ERNN algorithm is capable of simulating formed network parameters that can be used for training and validation so that the value of the network Mean Square Error (MSE) can be determined, with prediction accuracy using the Mean Absolute Percentage Error (MAPE) for forecast in sample in the forecast period of 5 years obtained an average of 0.77%, and able to know the dominant factors that influence the use of social tariff electricity.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright (c) 2022 Titik Rahmawati, Landung Sudarmana, Agung Priyanto