Short-Term Load Forecasting using Artificial Neural Networks techniques: A case study for Republic of 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. Results from the research show that the quality of the short-term prediction depends on the size of the data set and the data transformation.