A truly ubiquitous, planet-wide Internet of Things requires ultra-low-power, long-lasting sensor nodes at its core so that it can be practically utilized in real-world scenarios without prohibitively high maintenance efforts. Recent advances in energy harvesting and low-power electronics have provided a solid foundation for the design of such sensor nodes. However, the issue of reliable two-way communication among such devices is still an active research undertaking due to the high energy footprint of traditional wireless transceivers. Although approaches such as radio duty cycling have proved beneficial for reducing the overall energy consumption of wireless sensor nodes, they come with trade-offs such as increased communication latency and complex protocols.

Energy harvesting, batteryless devices can mitigate many shortcomings of battery-operated devices in the coming years, particularly in the area of the Internet of Things. Solar energy is an accessible and effective source of power for such devices. However, the uncontrollable nature of solar energy makes the task of designing and evaluating energy harvesting devices a challenging one. In this work, we propose PROGNOES, a tool that allows the user to forecast the harvestable solar energy by a particular solar cell at a specific time and location. To carry out the forecasts, various algorithms and models are used not only to provide the theoretical solar irradiation but also to provide the weather condition data corresponding to the time and location of the forecasting. PROG-NOES can generate forecasts in the form of IV curves, similar to those generated by physical solar cells. Such IV curves can then be emulated with suitable tools to provide energy traces to the energy harvesting devices at design time, therefore facilitating the task of designing robust, reliable energy harvesting, batteryless devices. In our experiments, we find that the energy forecasts carried out by PROGNOES can reach a mean absolute error of 7.16 mW.