Temperature Compensation Model for Monitoring Sensor in Steel Industry Load Management

Liyuan Sun; Zeming Yang; Nan Pan; Shilong Chen; Yaoshen He; Junwei Yang

doi:10.46604/ijeti.2024.13621

Authors

Liyuan Sun Measurement Center, Yunnan Power Grid Co., Ltd., Kunming, China
Zeming Yang Faculty of Civil Aviation and Aeronautical, Kunming University of Science & Technology, Kunming, China; LongShine Technology Group Co., Ltd., Wuxi, China
Nan Pan Faculty of Transportation Engineering, Kunming University of Science & Technology, Kunming, China
Shilong Chen Yuxi Power Supply Bureau, Yunnan Power Grid Co., Ltd., Yuxi, China
Yaoshen He Faculty of Transportation Engineering, Kunming University of Science & Technology, Kunming, China; LongShine Technology Group Co., Ltd., Wuxi, China
Junwei Yang LongShine Technology Group Co., Ltd., Wuxi, China

DOI:

https://doi.org/10.46604/ijeti.2024.13621

Keywords:

power systems, temperature compensation, SVR, improved whale optimization algorithm

Abstract

The iron ore industry faces increasing electricity demand due to industrialization, making effective management of electricity demand crucial. This study proposes a temperature compensation model using Support Vector Regression (SVR), aiming to enhance the accuracy of sensors in monitoring electricity demand. An experiment is conducted to assess the impact of temperature on sensor measurements, and a modified Whale Optimization Algorithm is employed to correct the sensor outputs. The proposed model is compared with both PSO-SVR and unimproved WOA-SVR. Results show that the proposed model significantly improves accuracy, achieving a determination coefficient of 0.7882 and a relative standard deviation of the error square sum of 4.6412%. The results of this study not only enhance power demand management in iron mining but also hold potential applications across various industries.

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