Calibration and Validation of INA219 as Sensor Power Monitoring System using Linear Regression

Abstract

Electricity demand which increases up to 2.7%, needs to be evaluated to prevent power wastage. This paper proposes an INA219 sensor and a power monitoring solution based on the ESP8266. Power Monitoring stores and displays real-time data in Google Sheets via Blynk version 1.0.1. The system has been calibrated with a fixed LED and resistor as a voltage calibration load. Meanwhile, the lamp and shunt resistors calibrate the shunt voltage. The measuring tools for comparison in calibration are digital multimeters, oscilloscopes, and power data loggers. Calibration using the linear regression technique with accuracy, precision, and uncertainty analysis are determined by Mean Absolute Percent Error (MAPE), Relative Standard Deviation (RSD), and Gaussian distribution. Successively, the sensor coefficient of determination (R2), accuracy, precision, and uncertainty of the load voltage and shunt voltage are 0.999 and 0.997, 99.27% ​​and 93.71%, 99.82% and 99.55%, 0.37 V and 0.89 mV.