Analysis of an adaptive time-series autoregressive moving-average (ARMA) model for short-term load forecasting

Regional load forecasting in Taiwan––applications of artificial neural networks

“…Time series methods treat the load pattern as a time series signal with known seasonal, weekly and daily periodicities [2][3][4]. These periodicities give a rough prediction of the load at the given season, day of the week and time of the day.…”

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

“…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.…”

A Novel Combined Approach for Daily Electric Load Forecasting Based on Artificial Neural Network and Modified Bat Algorithm

“…Authors believed that the average load forecasting error can be higher compared to the other days during week. In [21] an adaptive autoregressive moving-average model is introduced for electric load prediction and the superiority of their method is compared with the traditional Box-Jenkins transfer function approach. Recently, it is shown that Artificial Neural Network (ANN) has a potential capability of mapping nonlinear variables which are existed in the electric load data.…”