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SolarAnywhere® is commonly related to utility-scale or distributed PV, however our photo voltaic irradiance knowledge powers much more than photo voltaic vegetation. An amazing instance: Piotr Lichota on the Warsaw College of Expertise is utilizing SolarAnywhere to determine when to launch solar-assisted Unmanned Aerial Autos (UAVs), and the way lengthy they will realistically keep within the air.
Photo voltaic UAVs are being developed for autonomous environmental monitoring, precision agriculture and search-and-rescue. The success of this expertise, nevertheless, relies upon closely on climate. Climate unpredictability, corresponding to dynamic low-altitude cloud cowl in areas like Central Europe, can tremendously restrict their potential functions.
Modeling UAV flight period chances utilizing SolarAnywhere
To realistically assess whether or not a UAV can efficiently full a mission, Piotr realized he should discover a method to exactly replicate real-world atmospheric volatility. To maneuver previous theoretical clear-sky assumptions, he constructed a simulation framework on prime of the SolarAnywhere 25+ yr, satellite-derived irradiance dataset.
The method makes use of Most Chance Estimation to determine clear-sky DNI and DHI profiles. These profiles exhibit considerably decrease errors than the usual reference, long-term averages or typical meteorological yr formulations. The high-fidelity datasets had been additional used to construct a cloud cowl mannequin, as default clear-sky assumptions had been discovered to be inherently inadequate in areas characterised by excessive climate variability, corresponding to these in Central Europe.
With this stochastic framework, Piotr quantified the likelihood of reaching a goal flight period for a given time, location and flight configuration. As proven in Determine 1, a noon launch has meaningfully higher odds of an extended mission than an early-morning one—similar UAV, similar day. That’s the form of perception you may’t get from a clear-sky assumption.
Enabling next-generation inexperienced expertise
Utilizing this methodology, Piotr was in a position to exhibit how mission feasibility might be impacted by changes in solar-to-wing space ratios, battery capacities or the number of a selected takeoff hour. As an alternative of estimating an unrealistic theoretical most flight period beneath a transparent sky, the mannequin generated likelihood curves of mission success. Thus, the framework facilitates the transition in direction of risk-oriented design for inexperienced expertise functions.
This use of SolarAnywhere is a superb reminder that finance-grade photo voltaic useful resource knowledge isn’t only for photo voltaic builders—it may well assist design the following technology of inexperienced expertise, within the sky in addition to on the bottom.
We thank Piotr Lichota on the Warsaw College of Expertise, School of Energy and Aeronautical Engineering for the collaboration.
This analysis was funded in entire or partially by Nationwide Science Centre, Poland, 2025/09/X/ST8/00294.
Information from SolarAnywhere.
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