Estimation of global solar irradiance with LDR sensor and artificial neural network embedded in an 8-bit microcontroller

Abstract

This work deals with the estimation of global solar irradiance in an embedded platform through the use of a Light Dependent Resistor (LDR) sensor and its temperature. A prototype is built and has its values compared to the data obtained through a commercial pyranometer. An Multilayer Perceptron neural network is used to provide a non-linear regression between the voltage and temperature signals of the LDR on the commercial sensor irradiance data in a data analysis software. A regression with determination coefficient of 96.466 % and with mean squared error of 0.04 was obtained. The neural network present in the embedded system has 100% accuracy in relation to the neural network present in the data analysis software used. The minimum response time of the prototype is 13.49 ms and its dissipated power is 27.2 mW, making the approach quite promising. This neural estimation, using simple sensors like the LDR, can help to reduce the cost of renewable energy applications and make it easier to implement.