Solar irradiance forecasting services built for utility-scale and distributed photovoltaic plants are now being applied to solar-assisted unmanned aerial vehicles, with a Warsaw University of Technology researcher using SolarAnywhere data to determine optimal launch windows and realistic flight endurance for UAVs that harvest sunlight mid-flight. This crossover signals a maturing market where high-resolution, bankable solar resource data – historically a due-diligence tool for project finance – is becoming an operational input for mobile, energy-harvesting platforms that operate far from the grid.
From Fixed Arrays to Mobile Energy Harvesters
SolarAnywhere, a service operated by Clean Power Research, has long supplied satellite-derived irradiance time series and forecasts to developers, lenders, and independent engineers evaluating fixed-tilt and tracker PV projects. Its typical workflow involves delivering Typical Meteorological Year (TMY) datasets, sub-hourly historical records, and probabilistic forecasts that feed energy yield models like PVsyst or SAM. The Warsaw University of Technology application, led by Piotr Lichota, repurposes that same data chain for a fundamentally different physics problem: instead of sizing a stationary array for a 25-year power purchase agreement, the model must size wing-mounted cells and onboard storage for a vehicle that moves through changing sun angles, cloud fields, and atmospheric conditions in real time.
Solar-assisted UAVs – sometimes called high-altitude platform stations (HAPS) or pseudo-satellites – occupy a niche between conventional battery-electric drones and satellites. They carry photovoltaic cells across their wings or fuselage, charging batteries during daylight to sustain flight through night cycles. Endurance targets range from days to months, with applications in persistent surveillance, communications relay, atmospheric science, and remote sensing. The energy balance is unforgiving: every gram of structure, battery, and payload competes directly with the solar aperture and the aerodynamic efficiency needed to stay aloft at low power. Irradiance uncertainty translates directly into either excess dead weight (oversized batteries) or mission failure (undersized reserves).
Lichota’s work uses SolarAnywhere’s gridded historical and forecast data to simulate flight trajectories under realistic sky conditions, accounting for the UAV’s changing orientation, altitude, and geographic position. The service’s spatial resolution – on the order of 1-2 km for its latest satellite-derived products – and temporal granularity (down to 5-minute intervals in some forecast horizons) align with the timescales at which a slow-flying UAV experiences irradiance ramps from cloud edges. This is a stricter demand than fixed PV, where a passing cloud affects a stationary array for minutes; a UAV flying at 20-30 m/s can traverse a cloud shadow in seconds, creating sharp power transients that the power management system must buffer.
Convergence of Solar Forecasting and Aviation Meteorology
The Warsaw project illustrates a broader convergence: solar resource assessment is migrating from a pre-construction discipline into an operational intelligence layer for any system that converts photons to electrons in real time. Grid operators have already adopted intra-day and intra-hour solar forecasting for balancing markets; now, mobile platforms – UAVs, solar-electric marine vessels, even solar-integrated electric vehicles – are creating demand for forecasts that move with the asset. That points to a product evolution where irradiance services add trajectory-aware APIs, delivering not just “what is the GHI at this lat/lon” but “what is the effective POA irradiance on a surface with this azimuth, tilt, and velocity vector over the next six hours.”
By comparison, the aviation weather ecosystem has traditionally relied on METARs, TAFs, and numerical weather prediction (NWP) outputs tuned for visibility, ceiling, icing, and turbulence – not for downwelling shortwave flux at the surface. Solar data providers sit on a complementary stack: geostationary satellite retrievals (GOES, Himawari, Meteosat) calibrated against ground pyranometer networks, processed into cloud optical properties and aerosol optical depth, then radiatively transferred to surface irradiance. Merging these stacks – so that a UAV flight planner sees both convective initiation risk and the resulting irradiance deficit along a 3D trajectory – is a natural product integration that neither aviation weather vendors nor pure-play solar forecasters have fully delivered yet.
If this trend holds, the next 24-36 months could see solar forecasting platforms offering “energy-aware routing” as a standard module, analogous to how marine weather routing optimizes for fuel consumption. For a solar UAV, the cost function is not fuel but state-of-charge at sunset; the optimizer would trade altitude, speed, and heading to maximize integrated irradiance while avoiding cloud fields that force battery discharge. The Warsaw research is an early validation that the underlying irradiance data quality – already vetted by project finance – is sufficient for this new class of decision support.
Who This Affects
- Solar data providers (Clean Power Research, Solcast, Meteomatics, etc.): A new addressable market for API-driven, trajectory-aware irradiance services beyond stationary PV, with pricing models likely shifting from per-site licenses to per-flight or per-vehicle subscriptions.
- UAV and HAPS developers (Airbus Zephyr, AeroVironment, Skydweller, BAE Systems PHASA-35): Access to bankable, high-resolution historical irradiance enables more realistic endurance modeling during design phase, reducing risk of overweight battery packs that erode payload capacity.
- Grid operators and transmission owners: Solar-assisted UAVs equipped with LiDAR, thermal, or hyperspectral sensors can inspect thousands of kilometers of lines and rights-of-way per flight; reliable solar forecasting makes those inspection missions schedulable with confidence, reducing reliance on crewed helicopters.
- Defense and communications procurement offices: Persistent ISR (intelligence, surveillance, reconnaissance) and 5G/6G backhaul from stratospheric platforms depend on demonstrated multi-day endurance; irradiance-driven flight planning becomes a key milestone in capability demonstrations and contract awards.
What to Watch Next
- Publication of peer-reviewed results from the Warsaw University of Technology flight simulations, specifically the correlation between SolarAnywhere forecast skill and actual UAV state-of-charge trajectories in field trials.
- Integration of SolarAnywhere or competitor APIs into UAV ground control station software (e.g., QGroundControl, Mission Planner) as a native “solar endurance” layer alongside wind and weather overlays.
- First commercial HAPS service contracts that include guaranteed availability metrics tied to solar resource probability distributions – effectively bringing P50/P90 energy yield language into aviation service-level agreements.
- Extension of the same irradiance-data-for-mobile-platforms paradigm to solar-electric maritime (autonomous surface vehicles, solar ferries) and land transport (solar-assisted trucking, rail), where the value proposition is range extension rather than perpetual flight.
Bottom line: The same satellite-derived irradiance engine that de-risks billion-dollar solar farms is now de-risking gram-critical energy budgets on solar-powered aircraft – proving that bankable solar data has become a general-purpose infrastructure layer for any system that harvests sunlight in motion.
Read the full report at CleanTechnica
Note: facts and figures attributed above to reflect that outlet's original reporting. Broader context, cross-sector connections, and forward-looking scenarios reflect independent analysis by our editorial team.
About this article: Drafted by Energy Ai with AI-assisted research and writing based on public reporting, then reviewed under our editorial process before publication.
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