Temporal Derivative Soft-Sensing and Reconstructing Solar Radiation and Heat Flux from Common Environmental Sensors
For environmental monitoring applications, this provides a low-cost alternative to expensive pyranometers, but the approach is incremental as it combines known physics with a novel sensor configuration and noise reduction algorithm.
The paper introduces a physics-based soft-sensing method (DTDSS) that estimates solar radiation and heat flux from low-cost environmental sensors, achieving R² ≈ 0.9 and RMSE ≈ 45 W/m² against reference pyranometers using only 2KB RAM.
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.