As solar radiation is the most plentiful energy source on earth, thermoelectric energy harvesting emerges as an interesting solution for the Internet of Things (IoTs) in outdoor applications, particularly using semiconductor thermoelectric generators (TEGs) to power IoT devices. However, when a TEG is under solar radiation, the temperature gradient through TEG is minor, meaning that the TEG is useless. A method to keep a significant temperature gradient on a TEG is by using a solar absorber on one side for heating and a heat sink on the other side. In this paper, a compact TEG-based energy harvester that features a solar absorber based on a new class of solid matter, the so-called quasicrystal (QC), is presented. In addition, a water-cooled heat sink to improve the temperature gradient on the TEG is also proposed. The harvester is connected to a power management circuit that can provide an output voltage of 3 V and store up to 1.38 J in a supercapacitor per day. An experimental evaluation was carried out to compare the performance of the proposed QC-based harvester with another similar harvester but with a solar absorber based on conventional black paint. As a result, the QC-based harvester achieved 28.6% more efficient energy generation and achieved full charge of a supercapacitor around two hours earlier. At last, a study on how much the harvested energy can supply power to a sensor node for Smart agriculture during a day while considering a trade-off between the maximum number of measurements and the maximum number of transmission per day is presented.

Thermoelectricity energy harvesting based on the thermoelectric generator (TEG) has had a prominent role in energy harvesting solutions since they have dramatically grown recently due to the existence of significant thermal gradients $\Delta{{\mathrm {T}}}$ in both natural and industrial environments. However, to prove that a given environment is adequate to generate energy is a hard task. In order to early evaluate the potential of a given environment to get enough thermal energy to be converted to electric energy, we propose in this paper an automatic emulation system of thermal energy that can be used with any thermal source in a simple way. Experimental results show that the proposed platform can emulate any thermal gradient pattern, apply this pattern on a TEG and measure the actual electric energy generated by the TEG. An example of application is described, and the results are shown. The example is an experiment to harvest energy from tree trunks.

In the last decades, a lot of effort has been made in order to improve the use of environmentally friendly and renewable energy sources. In a context of small energy usage, energy harvesting takes place and thermal energy sources are one of its main energy sources because there are several unused heat sources available in the environment that may be used as renewable energy sources. To rapidly evaluate the energy potential of such thermal sources is a hard task, therefore, a way to perform this is welcome. In this work, a thermal pattern emulation system to evaluate potential thermal source in a easy way is proposed. The main characteristics of the proposed system is that it is online and remote, that is, while the thermal-source-under-test is being measured, the system is emulating it and evaluating the generated energy remotely. The main contribution of this work was to replace the conventional Proportional Integral Derivative (PID) controller to a Fuzzy-Proportional Integral (PI) controller. In order to compare both controllers, three tests were carried out, namely: (a) step response, (b) perturbation test, (c) thermal emulation of the thermal pattern obtained from a potential thermal source: tree trucks. Experimental results show that the Fuzzy-PI controller was faster than the PID, achieving a setting time 41.26% faster, and also was more efficient with a maximum error 53% smaller than the PID.