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2024 International Conference on Information Technologies

Short-term load forecasting using artificial neural network techniques: A case study for Republic of North Macedonia

Ana Kotevska
Nevenka Kiteva Rogleva
Faculty of Electrical Engineering and Information Technologies –Skopje
North Macedonia
Abstract:
Modernization and liberalization of power system in North Macedonia offers an opportunity to supervise and regulate the power consumption and power grid. This paper proposes models for short-term load forecasting using artificial neural network in order to balance the demand and load requirements and to determine electricity price. Neural network approach has the advantage of learning directly from the historical data. This method uses multiple data points. In this research, it can be confirmed that the quality of the short term prediction depends on the size of the data set and the data transformation.
Key words:
Artificial Neural Network (ANN)
Short Term Load Forecasting (STLF)
Back Propagation
Mean Absolute Percentage Error (MAPE)