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

共 50 条

[1] Bus Load Forecasting via a Combination of Machine Learning Algorithms
Panapakidis, Ioannis P.
Papagiannis, Grigoris K.
Christoforidis, George C.
2014 49TH INTERNATIONAL UNIVERSITIES POWER ENGINEERING CONFERENCE (UPEC), 2014,
[2] A comprehensive thermal load forecasting analysis based on machine learning algorithms
Leiprecht, Stefan
Behrens, Fabian
Faber, Till
Finkenrath, Matthias
ENERGY REPORTS, 2021, 7 : 319 - 326
[3] A Performance Comparison of Machine Learning Algorithms for Load Forecasting in Smart Grid
Alquthami, Thamer
Zulfiqar, Muhammad
Kamran, Muhammad
Milyani, Ahmad H.
Rasheed, Muhammad Babar
IEEE ACCESS, 2022, 10 : 48419 - 48433
[4] Forecasting the movements of Bitcoin prices: an application of machine learning algorithms
Pabuccu, Hakan
Ongan, Serdar
Ongan, Ayse
QUANTITATIVE FINANCE AND ECONOMICS, 2020, 4 (04): : 679 - 692
[5] Comparison of Machine Learning Algorithms for Structure State Prediction in Operational Load Monitoring
Mucha, Waldemar
SENSORS, 2020, 20 (24) : 1 - 26
[6] Multivariate machine learning algorithms for energy demand forecasting and load behavior analysis
Hussain, Farhan
Hasanuzzaman, M.
Abd Rahim, Nasrudin
ENERGY CONVERSION AND MANAGEMENT-X, 2025, 26
[7] Power System Load Forecasting Using Machine Learning Algorithms: Optimal Approach
Babu, M. Ravindra
Chintalapudi, V. Suresh
Kalyan, Ch. Nagasai
Bhaskar, K. Krishna
INTERNATIONAL JOURNAL OF RENEWABLE ENERGY RESEARCH, 2024, 14 (03): : 458 - 467
[8] Evaluation of electrical load demand forecasting using various machine learning algorithms
Jain, Akanksha
Gupta, S. C.
FRONTIERS IN ENERGY RESEARCH, 2024, 12
[9] Integrating the industrial consumer into smart grid by load curve forecasting using machine learning
Ungureanu, Stefan
Topa, Vasile
Cziker, Andrei
PROCEEDINGS OF 2019 8TH INTERNATIONAL CONFERENCE ON MODERN POWER SYSTEMS (MPS), 2019,
[10] Forecasting ETFs with Machine Learning Algorithms
Liew, Jim Kyung-Soo
Mayster, Boris
JOURNAL OF ALTERNATIVE INVESTMENTS, 2018, 20 (03): : 58 - 78

← 1 2 3 4 5 →