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.

Residential and agricultural buildings must prioritize environmental sustainability, employing locally sourced, bio/geologically sustainable materials, and reversible construction methods. Hence, adobe construction and earth-based building methods are experiencing a comeback. This article describes the hygrothermal performances of a real scale agricultural building prototype, in real field conditions, built and designed to be energy-efficient, environmentally friendly, and well-suited for the hot, dry climates typical of the Mediterranean region during summer. The building prototype is a small modular two room construction, one room based on wood (for control purpose) and the other one on raw earth. The experimental set up highlights the passive cooling and humidity regulation potential provided by raw earth and adobe brick technology in agricultural buildings used for fruit and vegetable storage. Such passive cooling alternatives in the Mediterranean climate could reduce the need for energy-intensive and environmentally impactful cold storage rooms.

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.