SVM Based Maximum Power Consumption Excess Forecast Alert for Large-Scale Power Consumers

Seigo Haruta, Ken-ichi Tokoro, Takashi Onoda

2023

Abstract

Large-scale power consumers, such as buildings and factories, make high-voltage power contracts with the Japanese electric power companies. The basic fee for high-voltage power contracts is based on the maximum power consumption in the past year. If the power consumption in the present month does not exceed the maximum power consumption in the past year, large-scale power consumers can suppress the basic fee. So, large-scale power consumers need the alert to prevent the maximum power consumption in the present month from exceeding the maximum power consumption in the past year. In this study, excess forecasting was performed considering the characteristics of power consumption in each industry. In addition, we proposed SVM improvements for imbalanced data. We applied this method to power consumption data, which is imbalanced data, to perform excess forecast. As a result, we have improved the accuracy of the excess forecast and contributed to effective alerts to many large-scale power consumers.

Paper Citation

in Harvard Style

Haruta S., Tokoro K. and Onoda T. (2023). SVM Based Maximum Power Consumption Excess Forecast Alert for Large-Scale Power Consumers. In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-626-2, pages 806-813. DOI: 10.5220/0011666100003411

in Bibtex Style

@conference{icpram23,
author={Seigo Haruta and Ken-ichi Tokoro and Takashi Onoda},
title={SVM Based Maximum Power Consumption Excess Forecast Alert for Large-Scale Power Consumers},
booktitle={Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2023},
pages={806-813},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011666100003411},
isbn={978-989-758-626-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - SVM Based Maximum Power Consumption Excess Forecast Alert for Large-Scale Power Consumers
SN - 978-989-758-626-2
AU - Haruta S.
AU - Tokoro K.
AU - Onoda T.
PY - 2023
SP - 806
EP - 813
DO - 10.5220/0011666100003411