Optimizing Short Term Load Forecast: A study on Machine Learning Model Accuracy and Predictor Selection

被引：0

作者：

Popovski, Pande ^{[1
]}

Veljanovski, Goran ^{[1
]}

Kostov, Mitko ^{[1
]}

Atanasovski, Metodija ^{[1
]}

机构：

[1] St Kliment Ohridski Univ, Fac Tech Sci Bitola, Sofia, Bulgaria

来源：

2022 57TH INTERNATIONAL SCIENTIFIC CONFERENCE ON INFORMATION, COMMUNICATION AND ENERGY SYSTEMS AND TECHNOLOGIES (ICEST) | 2022年

基金：

欧盟地平线“2020”;

关键词：

Short Term Load Forecast; Machine Learning; Regression analysis; Correlation analysis; Decision Trees; Support Vector Machines;

D O I：

10.1109/ICEST55168.2022.9828783

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：

This paper focuses on the importance of choosing the proper predictors when training a Machine Learning model for Short Term Load Forecasting, as well as to demonstrate the usefulness of Machine Learning in the field of power load forecasting. For the goals of the study, a correlation analysis was performed in order to observe the impact of some factors on the changes of power consumption. In addition, a number of models were created using machine learning where combinations of predictors were used based on their correlation to power load. The performance of these models was evaluated and the results are shown in this paper.

引用

页码：245 / 248

页数：4