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Short-Term Load Forecast in Electric Energy System in Bulgaria

Author(s): Irina Asenova | Dimitar Georgiev

Journal: Advances in Electrical and Electronic Engineering
ISSN 1804-3119

Volume: 8;
Issue: 4;
Start page: 102;
Date: 2010;
Original page

Keywords: Neural Networks | Electrical energy system | Load forecasting.

As the accuracy of the electricity load forecast is crucial in providing better cost effective risk management plans, this paper proposes a Short Term Electricity Load Forecast (STLF) model with high forecasting accuracy. Two kind of neural networks, Multilayer Perceptron network model and Radial Basis Function network model, are presented and compared using the mean absolute percentage error. The data used in the models are electricity load historical data. Even though the very good performance of the used model for the load data, weather parameters, especially the temperature, take important part for the energy predicting which is taken into account in this paper. A comparative evaluation between a traditional statistical method and artificial neural networks is presented.
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