TY - JOUR TI - Forecasting electricity load demand using hybrid exponential smoothing-artificial neural network model AU - Winita Sulandari; Subanar Subanar; Suhartono Suhartono; Herni Utami IS - Vol 2, No 3 (2016): November 2016 PB - Universitas Ahmad Dahlan JO - International Journal of Advances in Intelligent Informatics PY - 2016 SP - 131 EP - 139 UR - http://ijain.org/index.php/IJAIN/article/view/69/ijain_v2i3_p131_139 AB - Short-term electricity load demand forecast is a vital requirements for power systems. This research considers the combination of exponential smoothing for double seasonal patterns and neural network model. The linear version of Holt-Winter method is extended to accommodate a second seasonal component. In this work, the Fourier with time varying coefficient is presented as a means of seasonal extraction. The methodological contribution of this paper is to demonstrate how these methods can be adapted to model the time series data with multiple seasonal pattern, correlated non stationary error and nonlinearity components together. The proposed hybrid model is started by implementing exponential smoothing state space model to obtain the level, trend, seasonal and irregular components and then use them as inputs of neural network. Forecasts of future values are then can be obtained by using the hybrid model. The forecast performance was characterized by root mean square error and mean absolute percentage error. The proposed hybrid model is applied to two real load series that are energy consumption in Bawen substation and in Java-Bali area. Comparing with other existing models, results show that the proposed hybrid model generate the most accurate forecast