[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

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

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 79.170.44.78

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

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic

<style> #socialicons>a span { top: 0px; left: -100%; -webkit-transition: all 0.3s ease; -moz-transition: all 0.3s ease-in-out; -o-transition: all 0.3s ease-in-out; -ms-transition: all 0.3s ease-in-out; transition: all 0.3s ease-in-out;} #socialicons>ahover div{left: 0px;} </style>