A hybrid learning for neural networks applied to short term load forecasting
The aim of this work is to forecast total electric demand of Turkey one day in advance using neural networks. Starting with random weights and getting unrealistic forecasts could not be acceptable for a real-time operation; therefore, available past data, which is the actual load data obtained from Turkish Electricity Authority, is used off-line and the model is prepared for on-line forecasts. Moreover, a method based on hourly electric consumption is proposed to cluster input data. In order to have an idea about the success of the model, several alternate models are formed. The proposed model shows considerably better results.
hybrid learning short term load forecasting data clustering real-time process