Temporal Derivative Soft-Sensing and Reconstructing Solar Radiation and Heat Flux from Common Environmental Sensors

TL;DR

This paper introduces a physics-based soft-sensing method that estimates environmental energy flows, such as solar radiation and heat flux, using low-cost sensors and novel algorithms, validated through field testing.

Contribution

The study presents Differential Temporal Derivative Soft-Sensing (DTDSS), combining a new sensor configuration and INR algorithm to infer energy exchange parameters from simple environmental sensors.

Findings

Achieved R2 of approximately 0.9 in estimating GHI and heat flux.

Demonstrated the method's effectiveness on microcontrollers with under 2KB RAM.

Validated results using calibrated pyranometers in field tests.

Abstract

Modern methods of environmental monitoring are deficient in the lack of ability to take measurements of energy flows since traditional readings involve capturing parameters such as temperature, pressure, and humidity without considering their physical causes. The present research describes Differential Temporal Derivative Soft-Sensing (DTDSS), a physics-based approach which enables any ordinary low cost sensor array to infer estimates of the energy exchange in the environment by modeling its radiative heat fluxes. In particular, the proposed approach combines a novel paired sensor configuration along with a unique algorithmic solution called Inertial Noise Reduction or INR, that mathematically models the flow of energy in the environment by computing Global Horizontal Irradiance, or GHI, and convective heat flux. Experimental field testing has been conducted with the use of calibrated reference pyranometers supplied by the Department of Meteorology of Sri Lanka, yielding a correspondence between 8 bit embedded processor results and the reference of R2 approx. eqv. to 0.9 and RMSE approx. eqv. to 45 Watts per square meter in under 2KB RAM of a microcontroller unit.