Fractional integrated recurrent neural network (FIRNN) for forecasting of time series data in electricity load in Java-Bali

Abstract

The Increasing demand for electricity causes problems for State Electricity Company (SEC), which is called PLN in Indonesia in providing services to the public. Neural network (NN) is one of the methods that is often used in forecasting electricity load in different countries. Another form of neural network which is widely used for the analysis of issues that have a repeating pattern is the model of Recurrent Neural Network (RNN). Particularly, the problem in this paper is how to how to develop a model of Fractional Integrated Recurrent Neural Networks (FIRNN) in forecasting time series data on National Electricity Load and how are the forecasting results of time series data on national electricity load using Fractional Integrated Recurrent Neural Networks (FIRNN). Furthermore, RNN models in long memory nonlinear models in this study will be called Fractional Integrated Recurrent Neural Networks (FIRNN). This research was conducted with literature studies, simulations and applications to real cases in memory of long time series data, by taking the case of the burden of the use of electricity in Indonesia. The previous studies show that most of the time series on consumption patterns of electrical load in Semarang city shows the pattern of long memory, because it has a fractional difference parameter which can be seen in [26]. 1536 Walid et al. This study is aimed at assessing and developing a model of Integrated Fractional Recurrent Neural Networks (FIRNN). This study is a form of renewal of other studies, considering study long memory models using Neural Network has not been done by other researchers. RNN models used in this study is a model of Elman recurrent neural network (Elman-RNN). The results show that the forecasting by using FIRNN is much better in comparison with ARIMA models. It can be seen on the value of Mean Absolute Percentage Error (MAPE) and the Root of Mean Square Error (RMSE) that the results of prediction accuracy using FIRNN is better than forecast results using ARIMA.