Automatic Reactive Power Classification Based on Selected Machine Learning Methods

Viktor Pristaš; L’ubomír Antoni; Gabriel Semanišin

Research.Publish.Connect.

*Please fill out at least one Field. *Value must be an number!

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Name:
Country:
Subject:

Advanced Search Affiliations Search

If you're looking for an exact phrase use quotation marks on text fields.

Proceedings

Proceedings Search *Please fill out at least one Field. *Value must be an number!

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Papers

Papers Search *Please fill out at least one Field.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Authors

Authors Search *Please fill out at least one Field.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Advanced Search

Paper

Automatic Reactive Power Classification Based on Selected Machine Learning Methods

Topics: Big Data; Data Science; Machine Learning

In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, 100-107, 2023 , Lisbon, Portugal

Authors: Viktor Pristaš ; L’ubomír Antoni and Gabriel Semanišin

Affiliation: Institute of Computer Science, Faculty of Science, Pavol Jozef Šafárik University, Jesenná 5, 041 80, Košice, Slovakia

Keyword(s): Machine Learning, 5x2 Cross-Validation Test, Reactive Power, Power Factor, Electricity System.

Abstract: Reactive power is an important part of the electric power systems in order to rotate machines or to transmit active power by transmission lines. However, an excess of reactive power in the electrical systems can increase the risk of failure of the transmission system. We present an automatic reactive power classification on multifamily residential dataset of electricity based on selected machine learning methods. We aim to predict an excess of reactive power in the apartments located in the Northeastern United States. Moreover, we explore the statistical significance of differences between mean performances of selected machine learning methods.

CC BY-NC-ND 4.0

Guest: Register as new SciTePress user now for free.

SciTePress user: please login.

My Papers

You are not signed in, therefore limits apply to your IP address 3.144.107.32

In the current month:

Recent papers: 100 available of 100 total

2⁺ years older papers: 200 available of 200 total

Paper citation in several formats:

Pristaš, V. ; Antoni, L. and Semanišin, G. (2023). Automatic Reactive Power Classification Based on Selected Machine Learning Methods. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-623-1; ISSN 2184-433X, SciTePress, pages 100-107. DOI: 10.5220/0011619000003393

@conference{icaart23,
author={Viktor Pristaš and L’ubomír Antoni and Gabriel Semanišin},
title={Automatic Reactive Power Classification Based on Selected Machine Learning Methods},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2023},
pages={100-107},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011619000003393},
isbn={978-989-758-623-1},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Automatic Reactive Power Classification Based on Selected Machine Learning Methods
SN - 978-989-758-623-1
IS - 2184-433X
AU - Pristaš, V.
AU - Antoni, L.
AU - Semanišin, G.
PY - 2023
SP - 100
EP - 107
DO - 10.5220/0011619000003393
PB - SciTePress