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

Abstract

This paper introduces an innovative approach
utilizing the INA219 sensor and ESP8266 for an efficient power
monitoring system, complemented by straightforward
calibration and validation techniques. Real-time data is
seamlessly stored and displayed in Google Sheets through Blynk
apps. The system undergoes calibration using fixed DC lamps
and resistors as voltage loads, with digital multimeters,
oscilloscopes, and power data loggers employed for comparative
analysis. Calibration employs the linear regression technique,
and accuracy, precision, and uncertainty analyses are
determined through Mean Absolute Percent Error (MAPE),
Relative Standard Deviation (RSD), and Gaussian distribution.
Notably, the load voltage and shunt voltage sensor coefficients
of determination (R2
) stand at 0.999 and 0.997, with
corresponding accuracy rates of 99.27% and 93.71%, precision
levels of 99.82% and 99.55%, and uncertainties of 0.37 V and
0.89 mV. The research reveals a noteworthy finding: for
achieving accurate current measurements when employing an
external shunt resistor smaller than the INA219's internal shunt
resistor, calculating the current using Ohm's law, proves more
accurate than direct measurement.