#renewable-energy

Wood-pellet heating maths as an API, computed locally and deterministically — the consumption, heat-output and storage numbers a homeowner, installer or heating planner sizes a pellet system by. The consumption endpoint gives the pellets to meet a heat demand = the demand ÷ the usable heat per kilo, where usable = the calorific value × the boiler efficiency: ENplus wood pellets hold about 4.8 kWh/kg and a modern pellet boiler runs ~90 %, so each kilo delivers roughly 4.3 kWh — a 10,000 kWh annual demand then needs about 2.3 tonnes of pellets, around 154 fifteen-kilo bags or a bulk delivery. The heat-output endpoint inverts it: the usable heat from a mass = mass × calorific value × efficiency, so a tonne of ENplus pellets is about 4,800 kWh gross of which a 90 % boiler delivers ~4,320 kWh — the equivalent of roughly 480 litres of heating oil or 432 m³ of natural gas. The storage-volume endpoint sizes the hopper or silo: storage = the pellet mass ÷ the bulk (poured) density, about 650 kg/m³ for ENplus, so 2.3 tonnes fill roughly 3.6 m³ — size the store for the full delivery plus headroom for the fill pipe. Everything is computed locally and deterministically, so it is instant and private. Ideal for pellet-heating and installer tools, home-energy and quoting apps, and renewable-heat calculators. Pure local computation — no key, no third-party service, instant. Uses standard ENplus figures — set your own for a specific pellet grade. 3 compute endpoints. For cordwood use a firewood API; for propane/LPG a propane API.

api.oanor.com/pellet-api

Solar-array row-spacing and shading geometry as an API, computed locally and deterministically — the shadow-length, inter-row-spacing and ground-coverage numbers a PV designer or installer lays a ground-mount or flat-roof array out with. The shadow-length endpoint gives the shadow an object casts = its height ÷ tan(sun elevation), longer the lower the sun (which is why layouts are designed for the worst-case winter-solstice low sun), stretched by 1/cos(azimuth difference) when the sun is off-axis. The row-spacing endpoint gives the minimum row pitch (front edge to front edge) to stop a row shading the one behind = the module's horizontal base (length × cos tilt) + the shadow its back edge casts (module height ÷ tan of the minimum sun elevation) — a 1.7 m module at 30° tilt clearing a 20° winter sun needs about a 3.8 m pitch — and returns the resulting ground coverage ratio. The ground-coverage endpoint gives that GCR = module length ÷ row pitch, the packing density: fixed-tilt fields typically run 0.4–0.5, higher packs more kW per acre but loses winter yield to mutual shading, lower wastes land. Everything is computed locally and deterministically, so it is instant and private. Ideal for solar-design and layout tools, EPC and site-assessment apps, and renewable-energy calculators. Pure local computation — no key, no third-party service, instant. Geometric model — use the real worst-hour sun altitude. 3 compute endpoints. For solar position/altitude use a solar-position API; for irradiance a solar API; for off-grid sizing an off-grid API.

api.oanor.com/pvspacing-api

Off-grid solar system-sizing maths as an API, computed locally and deterministically — the battery-bank, solar-array and charge-controller numbers an RV, cabin, boat or off-grid homeowner sizes a system with. The battery-bank endpoint gives the storage you need = (daily load × days of autonomy) ÷ (depth of discharge × round-trip efficiency), then ÷ the system voltage for amp-hours: the autonomy carries you through cloudy days and the depth-of-discharge limit protects the cells (lead-acid ~50 %, lithium 80–100 %, which is why lithium banks run smaller), so a 2 kWh/day load at 12 V with 2 days autonomy, 50 % DoD and 85 % efficiency needs about 785 Ah. The array endpoint gives the panels = daily energy ÷ (peak sun hours × system efficiency), where peak sun hours is the day's irradiance as equivalent full-sun hours (~3–6 by place and season) and the efficiency rolls up controller, wiring, heat and dust losses — about 670 W for that load at 4 sun hours and 75 %. The charge-controller endpoint sizes the controller = array watts ÷ battery voltage × a 1.25 safety factor, so a 700 W array on a 12 V bank wants roughly an 80 A controller. Everything is computed locally and deterministically, so it is instant and private. Ideal for solar-installer and DIY tools, RV/marine/cabin power planners, and renewable-energy calculators. Pure local computation — no key, no third-party service, instant. Size for the worst month. 3 compute endpoints. For solar irradiance and sun hours use a solar API; for battery runtime under load a battery API.

api.oanor.com/offgrid-api

Hydroelectric-power engineering maths as an API, computed locally and deterministically. The power endpoint computes the electrical power a hydro plant generates with P = ρ·g·Q·H·η, from the water flow rate, the net head (the effective drop), the overall turbine-generator efficiency (typically 0.80–0.92) and the water density, returning both the gross power at 100 % efficiency and the net electrical output. The sizing endpoint inverts the relation to size a scheme — given a target power it solves the flow rate needed at a known head, or the head needed at a known flow, Q = P/(ρ·g·H·η). The annual-energy endpoint computes the yearly energy from the rated power and a capacity factor (typically 0.3–0.6 for hydro, accounting for water availability and downtime), E = P × 8760 h × capacity factor, and an optional revenue from an electricity price. Flow is in cubic metres per second, head in metres, efficiency 0–1, power in watts, kilowatts and megawatts. Everything is computed locally and deterministically, so it is instant and private. Ideal for renewable-energy, micro-hydro, civil-engineering, feasibility and sustainability app developers, run-of-river and reservoir tools, and energy education. Pure local computation — no key, no third-party service, instant. Live, nothing stored. 3 endpoints. This is hydroelectric generation; for wind-turbine power use a wind-power API, for solar resource a solar API and for pump (energy-consuming) duty a pump API.

api.oanor.com/hydropower-api

Wind-turbine power maths as an API, computed locally and deterministically. The power endpoint applies the wind-power equation P = ½ · ρ · A · v³ · Cp: from the wind speed, the rotor (given as swept area, diameter or blade length) and an optional air density and power coefficient, it returns the total power in the wind, the Betz maximum (the theoretical 16/27 ≈ 59.3 % limit) and the power actually extracted at the chosen coefficient — in watts, kilowatts, megawatts and horsepower. The energy endpoint multiplies power by time and an optional capacity factor to give the energy produced in watt-, kilowatt- and megawatt-hours, taking the power directly or deriving it from the wind and rotor. The sweptarea endpoint is a geometry helper: swept area from a diameter, radius or blade length, plus the blade-tip speed and tip-speed ratio from an rpm. Wind speed accepts metres per second, km/h, mph or knots; air density defaults to 1.225 kg/m³ at sea level. Because power scales with the cube of wind speed and the square of rotor diameter, small changes move it a lot — the API shows every intermediate value. Everything is computed locally and deterministically, so it is instant and private. Ideal for renewable-energy and engineering tools, education and physics apps, site-assessment and feasibility calculators, and STEM projects. Pure local computation — no key, no third-party service, instant. Live, nothing stored. 3 endpoints. This is wind-turbine power physics; for the Beaufort wind scale use a wind-scale API and for solar arrays use a solar API.

api.oanor.com/windpower-api

Solar irradiance and agroclimatology for any location on Earth — as an API over NASA POWER (Prediction Of Worldwide Energy Resources), derived from NASA satellite and reanalysis data. Get the solar resource needed to size and assess PV and CSP systems: global (GHI), direct-normal (DNI) and diffuse horizontal irradiance, clear-sky irradiance and the clearness index — either as long-term monthly climatology normals for quick site assessment, or as a daily time series for a date range (1981-present). The same call also serves meteorology — temperature, wind speed, relative humidity and precipitation — making it ideal for solar energy, agriculture, building-energy modelling and climate work. From cloudy Berlin to the Sahara, it turns a coordinate into bankable solar and climate data. A solar-resource / agroclimatology data source — distinct from PV-system energy simulation (PVGIS) and historical-weather records. Open data from NASA POWER.

api.oanor.com/solar-api

Solar photovoltaic potential for any location on Earth, powered by the EU JRC PVGIS (Photovoltaic Geographical Information System). Estimate how much energy a solar PV system would produce at a given coordinate — yearly and month-by-month output in kWh, the in-plane solar irradiation and a breakdown of system losses (angle-of-incidence, spectral, temperature) — for any panel size, fixed tilt and azimuth; find the optimal panel tilt and orientation that maximises annual output; and read the long-term monthly global horizontal solar irradiation. Covers most of the world (excluding polar and open-ocean areas) from years of satellite-based solar data. Ideal for solar installers and calculators, renewable-energy planning, home-energy and roof-potential tools, and climate / sustainability apps. Open data from EU JRC PVGIS.

api.oanor.com/pvgis-api