Automatic Reactive Power Classification Based on Selected Machine Learning Methods

Viktor Pristaš, L’ubomír Antoni, Gabriel Semanišin

2023

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.

Paper Citation

in Harvard Style

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, pages 100-107. DOI: 10.5220/0011619000003393

in Bibtex Style

@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},
}

in EndNote Style

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
AU - Pristaš V.
AU - Antoni L.
AU - Semanišin G.
PY - 2023
SP - 100
EP - 107
DO - 10.5220/0011619000003393