Implementation of K-Nearest Neighbor Regression for Forecasting Electricity Demand in Power System of Republic of North Macedonia

Load forecast is an important factor for operational and development planning of power system. Factors that play key role in forecasting power load consumption are the air temperature, type of the day (weekday, weekend or holiday), geographical differences, people standard, gross domestic product, demographic information, energy efficiency etc. The air temperature is one of the factors, which has significant impact on electricity consumption and power system load. This paper analyses the correlation between the power system load and the air temperature in Republic of North Macedonia. Furthermore, forecasting of the power system load is investigated. The power system load forecast is performed by applying k-nearest neighbor machine learning model. The power load depends on two variables – air temperature and date. Results show that for power load forecasts, k-nearest neighbor regression outperforms polynomial and sinuses regressions.