K-Nearest Neighbor Regression for Forecasting Electricity Demand

被引：0

作者：

Atanasovski, Metodija ^{[1
]}

Kostov, Mitko ^{[1
]}

Arapinoski, Blagoja ^{[1
]}

Spirovski, Mile ^{[1
]}

机构：

[1] Univ St Kliment Ohridski, Fac Tech Sci Bitola, Bitola, North Macedonia

来源：

2020 55TH INTERNATIONAL SCIENTIFIC CONFERENCE ON INFORMATION, COMMUNICATION AND ENERGY SYSTEMS AND TECHNOLOGIES (IEEE ICEST 2020) | 2020年

基金：

欧盟地平线“2020”;

关键词：

Power system load; Forecast; Machine learning; K-nearest neighbour; Correlation; Regression;

D O I：

10.1109/icest49890.2020.9232768

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：110 / 113

页数：4

共 18 条

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