K-Nearest Neighbor Regression for Forecasting Electricity Demand
Metodija Atanasovski, Mitko Kostov, Blagoja Arapinoski, Mile Spirovski
Abstract
Power system load forecasting plays a vital role in all aspects of power system planning, operation and control. It is a basic function for reliable and economical operation of power systems. This paper analyses the power system load forecast performed by applying k-nearest neighbour machine learning model, which is for the first time applied on real data of North Macedonia power system. The results are compared with polynomial and sinuses regressions.
Topics & Concepts
Electric power systemComputer sciencek-nearest neighbors algorithmFunction (biology)ElectricityPower (physics)RegressionDemand forecastingRegression analysisRegression functionArtificial intelligenceIndustrial engineeringData miningMachine learningEngineeringOperations researchStatisticsMathematicsElectrical engineeringEvolutionary biologyQuantum mechanicsPhysicsBiologyEnergy Load and Power ForecastingHydrological Forecasting Using AIStock Market Forecasting Methods