Low-cost sensors for indoor PV energy harvesting estimation based on machine learning

TL;DR

This paper introduces a low-cost indoor light energy harvesting sensor prototype that uses simple spectral measurements and machine learning to estimate harvestable energy with less than 5% error, enabling scalable IoT energy solutions.

Contribution

The study presents a novel, inexpensive sensor design combined with machine learning for accurate indoor energy harvesting estimation, reducing reliance on costly spectrometers.

Findings

Harvestable energy estimation error is less than 5% after 2 weeks.

The prototype uses commercial photodiodes and supervised machine learning.

The approach enables scalable deployment of indoor energy harvesters.

Abstract

With the number of communicating sensors linked to the Internet of Things (IoT) ecosystem in-creasing dramatically, well-designed indoor light energy harvesting solutions are needed. The first step towards this development is to determine the harvestable energy in real indoor environ-ments. But the harvestable energy varying over time with nature (spectra) and intensity of the light multi-sources, lighting data must be collected for sufficiently long periods. Besides, for a real implementation on-site, studies must be able to be carried out simultaneously in several places to determine locations with the highest energy harvesting potential. In this context, this manuscript presents a very low-cost prototype based on commercial photodiodes (rather than very expensive spectrometers), which measures only a very rudimentary number of spectral data. Thanks to a classification supervised machine learning from Matlab, in which an algorithm learns to classify new observations, and thanks to a simple principle of the superposition approximation model de-veloped for flexible GaAs solar cells, our harvestable energy estimation error is less than 5 percents after more than 2 weeks of observation. To measure this error, the data collected leading to an estimate of the harvestable energy is compared to what has been experimentally harvested in a real IoT system Li-ion battery and compared to what has been estimated using an expensive spectrometer during the same period. Our prototype should allow the development and the massive deploy-ment of a new generation of low cost indoor light energy harvesting sensors for future reliable indoor energy harvesters.