Low-Cost Sensors for Indoor PV Energy Harvesting Estimation Based on Machine Learning

Bastien Politi,Nicolas Camara,Alain Foucaran

doi:10.3390/en15031144

Bastien Politi, Nicolas Camara + Show 1 more

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https://doi.org/10.3390/en15031144

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Abstract

With the number of communicating sensors linked to the Internet of Things (IoT) ecosystem increasing dramatically, well-designed indoor light energy harvesting solutions are needed. A first step in this direction would be to be able to accurately estimate the harvestable energy in a specific light environment. However, inside, this energy varies in spectral composition and intensity, depending on the emission source as well as the time of day. These challenging conditions mean that it has become necessary to obtain accurate information about these variations and determine their impact on energy recovery performance. In this context, this manuscript presented a method to apply an innovative energy harvesting estimation method to obtain practical and accurate insight for the design of energy harvesting systems in indoor environments. It used a very low-cost device to obtain spectral information and fed it to supervised machine learning classification methods to recognize light sources. From the recognized light source, a model developed for flexible GaAs solar cells was able to estimate the harvestable energy. To validate this method in real indoor conditions, the estimates were compared to the energy harvested by an energy harvesting prototype. The mean absolute error percentage between estimates and the experimental measurements was less than 5% after more than 2 weeks of observation. This demonstrated the potential of this low-cost estimation system to obtain reliable information to design energetically autonomous devices.

Highlights

The aim of this paper is to demonstrate the possibility of using the power estimation method and system in real-world conditions
A second look at the results showed that some classifiers seemed more adapted to our application: Cubic Support Vector Machine (SVM), Fine K-Nearest Neighbors (KNN) or Weighted KNN
We explored, for the first time, an ultra-low-cost light sensor device capable of estimating the harvestable energy in real indoor conditions

Summary

Introduction

The development of the Internet of Things (IoT)- or Wireless Sensor Networks (WSNs)based applications is growing significantly. Reaching energetic autonomy for the associated sensors remains a challenge, . A lot has been done to reduce their power consumption. A growing community of researchers has been working on improving the technologies to harvest enough power from the nearby environment to supply electrical energy to such devices [1,2]. One of the most common means to achieve such energy recovery is photo-electrical conversion, based on photovoltaic technologies. Databases and software have been used to accurately estimate the energy that can be harvested outdoors from square meters of sun light, anywhere on earth [3]. Under indoor light conditions, no standards have yet been created

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