FORECASTING ELECTRICITY SUPPLIED IN TURKEY USING HOLT-WINTERS’ MULTIPLICATIVE METHOD AND ARTIFICIAL NEURAL NETWORK (ANN) MODELS - AMRAN, SITI NOR ZULAIKA; MOHAMED, NORIZAN (Universiti Malaysia Terengganu Journal of Undergraduate Research, 2021)
Electricity is one of the most essential necessities in today’s world and has an important role for the development of societies and economics. The need for electricity is expanding continuously due to increasing population, urbanization and industrialization. Hence, the purpose of this study was to develop the best model for forecasting electricity supplied in Turkey by applying the multiplicative Holt-Winters method and multilayer feed-forward neural network model. The monthly electricity supplied in Turkey from January 2000 until December 2019 were obtained from monthly electricity statistics report presented by the International Energy Agency (EIA). The data were divided into two sets comprising in-sample data from January 2000 until December 2015 and out-sample data from January 2016 to December 2019. The multiplicative Holt-Winters was used since the electricity supplied in Turkey exhibit trend and seasonal gave the out-sample forecast of 3.6990. The best multilayer feed forward neural network (MFFNN) model with three input lag variable, one hidden node, one output node, sigmoid transfer function in hidden layer and linear transfer function in output layer gave the out-sample forecast of 2.1483. Hence it can be concluded that, the multilayer feed-forward neural network model is more accurate than multiplicative Holt-Winters method to forecast the electricity supplied in Turkey.