open-meteo-solar-forecast 0.1.32

Python API fetching Solarpanels forecast information.

About

With this python library you can request data from Open-Meteo and see what your solar panels may produce in the coming days.

Installation

pip install open-meteo-solar-forecast

Data

This library returns a lot of different data, based on the API:

Energy

• Total Estimated Energy Production - today/tomorrow (kWh)
• Estimated Energy Production - This Hour (kWh)
• Estimated Energy Production - Next Hour (kWh)
• Estimated Energy Production - Remaining today (kWh)
• Estimate.wh_period_15m: exact modeled energy in Wh for each 15-minute interval, keyed by its timezone-aware interval-start timestamp. It is derived from the interval-average modeled power after array combination and inverter clipping. Estimate.watts is instantaneous modeled power and is not an equivalent source for interval energy.

Power

• Highest Power Peak Time - Today (datetime)
• Highest Power Peak Time - Tomorrow (datetime)
• Estimated Power Production - Now (W)
• Estimated Power Production - Next Hour (W)
• Estimated Power Production - In +6 Hours (W)
• Estimated Power Production - In +12 Hours (W)
• Estimated Power Production - In +24 Hours (W)

API Info

• Timezone
• Rate limit
• Account type
• Rate remaining

Validation

• API key (bool)
• Plane (bool)

Example

import asyncio

from open_meteo_solar_forecast import OpenMeteoSolarForecast


async def main() -> None:
    """Show example on how to use the library."""
    async with OpenMeteoSolarForecast(
        latitude=52.16,
        longitude=4.47,
        declination=20,
        azimuth=10,
        dc_kwp=2.160,
		use_horizon=True,
		partial_shading=True,
        horizon_map=((0,30),(360,30),
		max_snowcover_depth_cm=5.0,
    ) as forecast:
        estimate = await forecast.estimate()
        print(estimate)


if __name__ == "__main__":
    asyncio.run(main())

Multiple PV arrays

To calculate a combined forecast from multiple arrays, pass per-array values as lists or tuples for the required parameters (latitude, longitude, declination, azimuth, and dc_kwp).

async with OpenMeteoSolarForecast(
    latitude=[52.16, 52.16],
    longitude=[4.47, 4.47],
    declination=[20, 35],
    azimuth=[-90, 90],  # east and west (0 = south, -90 = east, 90 = west)
    dc_kwp=[2.4, 1.8],
    # Optional per-array values can also be lists/tuples
    efficiency_factor=[0.95, 0.97],
    use_horizon=[False, True],
    horizon_map=[
        ((0, 0), (360, 0)),
        ((0, 20), (180, 10), (360, 20)),
    max_snowcover_depth_cm=[5.0,5.0],
    ],
) as forecast:
    estimate = await forecast.estimate()

Scalar values are still supported and are automatically applied to all arrays when mixed with list/tuple inputs.

Multiple inverters

If each array is connected to its own inverter, pass ac_kwp as a list/tuple with one capacity per array. Each array's output is then clamped to its own inverter capacity before the outputs are combined:

async with OpenMeteoSolarForecast(
    latitude=[52.16, 52.16],
    longitude=[4.47, 4.47],
    declination=[20, 35],
    azimuth=[-90, 90],
    dc_kwp=[2.4, 1.8],
    ac_kwp=[2.0, 1.5],  # one inverter per array
) as forecast:
    estimate = await forecast.estimate()

Use None for arrays without an inverter limit, e.g. ac_kwp=[2.0, None]. A scalar ac_kwp keeps the previous behaviour: a single inverter shared by all arrays, clamping the combined output.

The horizon map is a tuple of 2-tuples, where each 2-tuple consists of (azimuth,elevation). Azimuth is the compass direction in degrees (0° = north, 180° = south). The horizon map has to cover the whole range of azimuths that the sun travels through over the year (recommendation: plot the horizon from 0 to 360°). Elevation is the associated angle in degrees of any object (hill, tree, ...) casting a shadow on the modules. The elevation angle has to be in the range 0° (flat, ideal horizon) to 90° (in the sky directly over the modules). The map has to be monotonic on the azimuth axis, however this is not checked by the script! Elevation values in between are interpolated along the azimuth axis, thus non-monotonic values will give wrong results. The horizon map can also be passed from a text file, see the included example estimate_horizon.py.

If partial_shading is disabled and a shadow is detected on the module, only the diffuse irradiation will be used to calculate the power output. This is useful if the shading is predominantly from far-away objects, which can be treated as shading the whole module at once or not. If partial_shading is enabled and a shadow is detected on the module, the shadow is treated as partial. This is useful if the shading arises from close-by objects, which cast 'hard' contoured shadows on the module. In this case, an experimental calculation is used taking into account the 'sunniness' of the conditions. This is done via the ratio of diffuse and direct irradiation. A large share of diffuse irradiation (cloudy day) will let the module run as homogeneously shaded at diffuse power. A small share of diffuse irradiation (sunny) day will reduce the diffuse power even more, since hard partial shadows can shut down the module completely.

If tracking is set to a value other than "none", the irradiance is calculated for a panel that follows the sun on the given axis. "azimuth" models a vertical-axis (east-west) tracker and ignores the azimuth parameter, "tilt" models a tilt-axis tracker and ignores the declination parameter, and "dual" models a dual-axis tracker and ignores both. Like the other per-array parameters, it can be passed as a list/tuple for multiple arrays.

If max_snowcover_depth_cm is set to a value > 0, this value will be interpreted as the snow cover depth (in centimeters) that results in zero power from the module. In between, a linear interpolation over the forecast snow cover depth is applied. (A value of 0 turns this feature completely off.)

Contributing

Would you like to contribute to the development of this project? Then read the prepared contribution guidelines and go ahead!

Thank you for being involved! :heart_eyes:

Setting up development environment

This Python project uses uv as its dependency manager.

You need at least:

• Python 3.11+
• uv

Install all packages, including all development requirements:

uv sync

uv creates a virtual environment in .venv where it installs all necessary packages. Run commands inside it with:

uv run python

License

MIT License

Copyright (c) 2021-2024 Klaas Schoute
Copyright (c) 2024 Rany

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.