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PT. PLN (Perusahaan Listrik Negara) is Government Corporation that supplies electricity needs in Indonesia. This electricity needs depend on the electronic tool used by public society, so that PLN must fit public electricity demands from time to time. PLN works by predicting electricity power which is consumed by customers hourly. The prediction made is based on prior electricity power use. The prediction of amount of electricity power use is done to optimize electricity power used by customers, so that there will not be any electricity extinction. There are some methods that could be used for forecasting of amount of electricity power use, such as double seasonal ARIMA model and Neural Network (NN) method. Some researches that are related to short-term electricity power forecasting can be seen in Chen, Wang and Huang (1995), Kiartzis, Bakirtzis and Petridis (1995), Chong and Zak (1996), Tamimi and Egbert (2000), Husen (2001), Kalaitzakis, Stavrakakis and Anagnostakis (2002), Taylor (2003), Topalli and Erkmen (2003), Taylor, Menezes and McSharry (2006), and Ristiana (2008). Neural network methods used in those researches are Feed Forward Neural Network, which is known as AR-NN model. This model cannot get and represent moving average order effect in time series. Some prior researches in many countries in the world including in Indonesia showed that ARIMA model for the electricity consumption data tends to have MA order (see Taylor, Menezes and McSharry (2006) and Ristiana (2008)). The aim of this research is to study further about other NN type, i.e. Elman-Recurrent Neural Network (RNN) which can explain both AR and MA order effects simultaneously for forecasting double seasonal time series, and compare the forecast accuracy with double seasonal ARIMA model. As a case study, we use data of hourly electricity load demand in Mengare, Gresik, Indonesia. The results show that the best ARIMA model for forecasting these data is ARIMA ([1,2,3,4,6,7,9,10,14,21,33],1,8)(0,1,1)24(1,1,0)168. This model is a class of double seasonal ARIMA, i.e. daily and weekly seasonal with 24 and 168 length of periods respectively. Additionally, there are 14 innovational outliers detected from this ARIMA model. In this study, we apply 4 different architectures of RNN particularly for the inputs, i.e. the input units are similar to ARIMA model predictors, similar to ARIMA predictors plus 14 dummy outliers, the 24 multiplied lagged of the data, and the combination of 1 lagged and

Double Seasonal Recurrent Neural Networks for Forecasting Short Term Electricity Load Demand in Indonesia