| Short circuit (IEC 60909) | pandapower and a hand calculation, 154 / 33 kV test network | Ik″ max 13.85 kA — identical |
| Sag-tension (state change) | Independent exact-catenary calculation, ACSR conductor, 300 m span | Sag at 75 °C 8.530 m — identical |
| Water network solver (GGA) | EPANET 2.2 (via WNTR), two-loop benchmark network | Max head difference 0.0003 m |
| Storm sewer routing | SWMM 5 (via PySWMM), same network | Outfall peak 862 vs 860 L/s |
| Steam properties (IAPWS-IF97) | IAPWS verification tables | Relative deviation ≤ 2 × 10⁻⁹ |
| Hydrogen storage density | CoolProp, 350 bar | 23.995 kg/m³ — identical |
| GeoTIFF reader | GDAL on six reference rasters (LZW, Deflate, PackBits, BigTIFF, predictors) | Identical values |
| Mass haul optimisation | OR-Tools min-cost flow; textbook transportation problems | Same optimum (relative 5 × 10⁻¹⁶) |
| Point-cloud ground filter | Synthetic terrain with known ground and objects | Ground recall 99.98 %, DTM RMSE 0.07 m |
| Battery dispatch | Analytic two-price arbitrage | Reproduced exactly |
| PV energy yield | Commercial satellite-data yield assessment of a bifacial tracker plant (licensed data) | Green band — values not published (licensed reference) |
| Wind energy yield | Published per-turbine yield of a 6.25 MW low-wind site | 21.15 vs 20.09 GWh (+5 %) |
| Project finance model | Published feasibility study of a 400 MW wind farm | IRR 10.7 vs 10.3 %; LCOE 88.00 vs 88.06 USD/MWh |
| Hydropower study method | Replication of a feasibility study; EPC bill of quantities | Energy 48.7 vs 49.3 GWh; cost within 7 % of the BoQ |
| Substation estimator | Awarded tender for a 154 / 33 kV GIS substation; turnkey offer for a 400 kV GIS | 53.7 vs 53 k USD/MVA; within 13 % |
| PV energy yield (same inputs) | NREL PySAM Pvwattsv8 7.1 and pvlib 0.16 ModelChain; 10 MWp fixed tilt and tracker on one typical year | Specific yield 1.8–3.3 % below the two tools (WaiMAP adds row and diffuse shading, IAM, own inverter model) |
| Bifacial rear side (infinite sheds) | pvlib 0.16 infinite_sheds; 10 MWp bifacial tracker on a 10 % slope, same geometry | Rear gain 5.76 vs 5.76 % (+0.07 %); rear irradiation 177.6 vs 177.6 kWh/m² |
| Inverter clipping (Sandia model) | pvlib 0.16 inverter.sandia and NREL PySAM 7.1 Pvsandiainv on the same hourly DC power | 31.11 MWh — identical (both tools) |
| Slope-aware backtracking | pvlib 0.16 tracking.singleaxis with cross-axis tilt (Anderson & Mikofski 2020), same sun angles | Largest hourly angle difference 0.0002°; row shading identical |
| Stringing, row shading, pile grading | Hand calculations (IEC 62548-1 Voc at −10 °C; shaded fraction geometry; pile reveal on a 12 % slope) | 27 modules; shaded fraction 0.3246; 13.44 m³ — identical |
| Wind wake losses (same inputs) | PyWake 2.6 NOJ / Jensen, 5 turbines, same hourly series, curves and layout | Gross AEP identical; wake loss 3.11 vs 3.11 %, net AEP within 0.004 % |
| Wind Gaussian wakes (same settings) | PyWake 2.6 BastankhahGaussianDeficit, 5 and 20 turbines and an optimised layout, same hourly series | Wake loss 2.71 vs 2.70 % and 5.38 vs 5.37 %; net AEP within 0.02 % |
| Battery dispatch optimum | PyPSA 1.2 linear programme with HiGHS; 100 MW / 2 h on a synthetic price year | WaiMAP 0.35 % below the LP optimum (an upper bound) |
| Load flow | pandapower 3.5 and PyPSA 1.2 AC power flow, radial plant connection | Bus voltages within 0.003 %, losses within 0.005 % |
| Line sag-tension (same inputs) | Independent Python change-of-state solver (exact catenary length + elastic strain integral); ACSR Drake tension section, six IEC 60826 weather cases | Tensions and sags within 0.06 % (e.g. sag at 80 °C 12.605 vs 12.606 m) |
| Conductor ampacity (IEEE 738) | linerate 5.0 IEEE738 model, same conductor, weather and solar input | 721.3 vs 721.6 A at 75 °C; 977.1 vs 977.3 A at 100 °C (−0.03 %) |
| Optimum tower spotting | Exhaustive enumeration of every feasible tower sequence on two small profiles (same constraints) | Same minimum cost (233,929 and 194,067 USD) |