Temperature Compensation Model for Monitoring Sensor in Steel Industry Load Management

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.