Elman recurrent Neural Networks
By: Alfonsus J. Endharta and Suhartono
Neural networks have apparently enjoyed considerable success in practice for predicting short-term hourly electricity demands in many countries. In Indonesia, forecasting of short-term hourly electricity usually is done by employing classical methods such as Double Seasonal ARIMA model. Though there are researches held in Indonesia about electricity demand forecasting with neural networks, especially Feed-Forward Neural Networks (FFNN), researchers decided that classical methods such as ARIMA model perform better than neural networks because FFNN yields more MAPE than ARIMA model. Electricity demands in most city in Indonesia has moving average order that can not be represented by FFNN. In this paper we use Elman-Recurrent Neural Network (RNN), that able to represent moving average order. With this paper we prove if Elman-RNN is a better method in forecasting hourly electricity demands in Mengare, Gresik than ARIMA model. We get ARIMA ([1, 2, 3, 4, 6, 7, 9, 10, 14, 21, 33], 1, 8)(0, 1, 1)24(1, 1, 0)168 as the best Double Seasonal ARIMA model in forecasting electricity demands in Mengare. There are 14 innovational outliers detected from this model. We use 4 different architectures of neural networks. First one is that the input units are similar with ARIMA model predictors. Second, the ARIMA predictors plus 14 dummy outliers be the input units. Third, the 24 multiplied lagged of the data. And the last, the inputs are the 1 lagged and the 24 multiplied lagged plus minus 1. The best network we get is the last one, i.e., Elman-RNN(22, 3, 1). By comparing the MAPE of both methods, Elman-RNN perform well and bring less MAPE than ARIMA model.
Source: Alfonsus J. Endharta
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