Intelligent short-term load forecasting in Turkey

2006-09-01
Topalli, Ayca Kumluca
Erkmen, İsmet
Topalli, Ihsan
A method is proposed to forecast Turkey's total electric load one day in advance by neural networks. A hybrid learning scheme that combines off-line learning with real-time forecasting is developed to use the available data for adapting the weights and to further adjust these connections according to changing conditions. Data are clustered due to the differences in their characteristics. Special days are extracted from the normal training sets and handled separately. In this way, a solution is provided for all load types, including working days, weekends and special holidays. A traditional ARMA model is constructed for the same data as a benchmark. Proposed method gives lower percent errors all the time, especially for holidays. The average error for year 2002 is obtained as 1.60%.