Application of Machine Learning Algorithms for Operational Forecasting Load Curve

被引：0

作者：

Cheremnykh, Anton ^{[1
]}

Sidorova, Alena ^{[1
]}

Tanfilyev, Oleg ^{[1
]}

Rusina, Anastasia ^{[1
]}

机构：

[1] Novosibirsk State Tech Univ, Fac Power Engn, Novosibirsk, Russia

来源：

2021 62ND INTERNATIONAL SCIENTIFIC CONFERENCE ON INFORMATION TECHNOLOGY AND MANAGEMENT SCIENCE OF RIGA TECHNICAL UNIVERSITY (ITMS) | 2021年

关键词：

operational forecasting; load curve; state estimation IPS; machine learning; NEURAL-NETWORK; OPTIMIZATION;

D O I：

10.1109/ITMS52826.2021.9615293

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Forecasting load curves is the most important task in the electrical energy industry. The quality of forecasting load curves depends on reliability and efficiency of power distribution, management of normal and post emergency modes, taking into account the interests of the subjects of the electrical power and capacity market. The article reviewed the issues of forecasting modes parameters on the operational time-frame of control by machine learning methods. The article are considered the forecasting load curves algorithms, which allow load curves to adapt the changes in the structure and energy consumption mode. This fact improves the quality of the operational management of the power system.

引用

页数：5

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