Short-Term Load Forecast in Power Systems: A Comparison of Different Practical Algorithms

Electricity demand forecasting has significant impact on planning and operation of a power system. Parameters that affect short and long-term load forecasting are the temperature, calendar day, geographical variations, gross national product, socio-demographic trends, energy efficiency etc. The weather conditions seriously affect load demand on short term. This paper focuses on a comparison of different practical methodologies for short-term load forecast and their application and implementation on real load demand and temperature data for Republic of North Macedonia. The paper provides detailed comparison of several practical algorithms for short-term load forecast: polynomial and sinuses regression, machine learning and artificial neural networks. The power load is considered from the aspect of two variables – temperature and calendar date. A case study is presented and results are discussed and analysed. Finally, conclusion and recommendations are presented.