One contract, one point of accountability
No gaps between the mast vendor, land agent and engineer. Scope, schedule and liability sit in one place.
Estimate a yield, read a sample report, explore site climate analytics, and see inside the turbine and the solar station.
Real-time surface wind from Open-Meteo, rendered as flowing particles the way our engineers read a site. Share your location or click anywhere on the map, and we fit a Weibull distribution to the coming week and estimate the resource at hub height.
Terrain, wind resource, exclusion zones and wakes on one canvas. Drag any turbine, or let the optimiser search thousands of layouts under real constraints: 3-rotor-diameter spacing, 500 m from the village, 150 m from the road. Every move re-runs the wake model.
A first-pass estimate from your measured wind speed, extrapolated to hub height with a Weibull distribution and a generic power curve. The bankable number comes from a full campaign; this tells you whether the site is worth one.
Assumes cut-in 3 m/s, cut-out 25 m/s, Cp 0.45, air density 1.225 kg/m³, grid factor 0.71 tCO₂/MWh, 1,200 kWh per household. Not a substitute for a bankable energy yield assessment.
Commission the full assessmentEvery campaign streams into a client portal with daily quality checks, cleaned exports and reproducible reports. The three cards below pull real-time conditions at our reference sites; the dashboard beneath is a sample campaign view.
Flip through an abridged sample of our energy yield assessment. Every number traces back to a calibrated sensor and a documented method, which is why lenders' engineers sign off quickly.
| Parameter | Value |
|---|---|
| Mast height / type | 120 m guyed lattice |
| Measurement period | 14 months |
| Data recovery, primary anemometer | 99.6% |
| Mean wind speed, 120 m | 7.62 m/s |
| Weibull A / k, 120 m | 8.60 m/s / 2.08 |
| Shear exponent 80–120 m | 0.19 |
| Turbulence intensity at 15 m/s | 9.8% |
| LiDAR verification, 100 m | R² 0.99, slope 1.004 |
| Sensor calibration | MEASNET, certificates appended |
| Item | Factor | GWh/yr |
|---|---|---|
| Gross energy, free stream | — | 262.4 |
| Wake losses | 93.9% | −16.0 |
| Availability | 97.0% | −7.4 |
| Electrical efficiency | 97.8% | −5.3 |
| Curtailment, environmental, other | 98.9% | −2.6 |
| Net energy, P50 | 88.0% | 231.1 |
| Net capacity factor | 31.4% |
| Source | σ | |
|---|---|---|
| Wind speed measurement | 2.1% | |
| Long-term representativeness | 2.8% | |
| Vertical extrapolation | 2.4% | |
| Spatial extrapolation | 3.0% | |
| Wake and loss models | 1.5% | |
| Combined, wind speed | 6.1% | |
| Combined, energy (sensitivity 1.6) | 9.8% |
Exceedance values derived from a normal distribution about the P50; 10-year uncertainty reduces long-term and interannual terms.
A nacelle-mounted Doppler LiDAR campaign on a six-turbine 6.3 MW fleet: static yaw misalignment measured against free-stream inflow, corrected through a per-direction yaw offset table, and the gain verified by re-measurement rather than assumed.
| Turbine | Misalignment | Gain after correction |
|---|
Gain calculated as cos3.5 of the corrected misalignment; euro values are computed live from the rating, capacity factor and price you set. Method: measure inflow and yaw per turbine over a representative period; separate misalignment from wake and terrain effects against SCADA; propose one yaw offset per wind direction per turbine; obtain OEM acceptance; implement static wake steering; verify by re-measurement. System deployed: EPSILINE WindEagle nacelle LiDAR, 2-second sampling, independent of turbine SCADA.
The analyses behind every yield number, drawn from the same portal your consultants use. Sample data from a 120 m campaign in Kutch.
Select a component to see what it does and how our measurement work informs it, from site suitability to power curve verification.
| IEC 61400-1 class | Reference wind Vref | Annual mean, typical | Turbulence categories (Iref) | Typical sites |
|---|---|---|---|---|
| Class I | 50 m/s | 10 m/s | A+ 0.18 · A 0.16 · B 0.14 · C 0.12 | Coastal, offshore, high plateaus |
| Class II | 42.5 m/s | 8.5 m/s | A+ 0.18 · A 0.16 · B 0.14 · C 0.12 | Most of Gujarat, Tamil Nadu, Karnataka |
| Class III | 37.5 m/s | 7.5 m/s | A+ 0.18 · A 0.16 · B 0.14 · C 0.12 | Inland low-wind sites, tall towers, large rotors |
| Class S | Site-specific | — | Designer-defined | Cyclone-prone coasts, extreme terrain |
Solar radiation resource assessment (SRRA) stations to ISO 9060 Class A, with BSRN-grade data handling. Select a sensor to see what it measures and why lenders ask for it.
No gaps between the mast vendor, land agent and engineer. Scope, schedule and liability sit in one place.
Raw and cleaned datasets in open formats with full audit trails, so any independent reviewer can reproduce our results.
In-house crews, customs experience and local partners for deserts, high plateaus, coastal and offshore work.
Every campaign is designed with the bankability review in mind, cutting weeks from the diligence cycle.