Abstract

The electricity demand has become increasingly significant for the financial decision makers with rapid economic growth. In order to achieve a sustainable economic growth, continuous and adequate power supply is crucial. Due to the electricity is unable to be stored economically and has a characteristic of coincidence of generation and consumption, forecasting electricity demand accurately is of great importance in order to balance supply and demand. Turkey, an emerging market with one of the most rapid economic growth rate in the world, should consider forecasting the gross electricity demand. As it is known, there exists a high correlation between growth rate of gross domestic product (GDP) and electricity demand in developing countries. Therefore, unlike many other forecasting models for electricity demand, a single parameter (GDP in line with the purchasing power parity) has been used to estimate gross annual electricity demand of Turkey in this empirical study. Three different forecasting methods, namely; time series, regression and fuzzy logic techniques have been applied to Turkish electricity demand data and then compared according to the absolute relative errors (AREP). Based on the AREP figures, it can be concluded that time series model has shown a slightly better forecasting performance than the other two methods for estimating gross annual electricity demand of Turkey based on the available data.

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