Energy Indicators (formely Energy Onshore)

This repository contains the scripts related to the Energy Onshore - Energy indicators use case of the Climate Adaptation Digital Twin (Climate DT). All the work is being developed in the frame of the Destination Earth initiative from the European Commission, where ECMWF is one of the Entrusted Entities.

LICENSE NOTE: the European Union, represented by the European Commission is the direct and sole owner of the intellectual property rights of these Results.

Description

The Energy Onshore application is currently being developed as a Python package, with two core scripts, wind.py, containing a comprehensive set of wind energy indicators and solar.py, containing a limited set of solar energy indicators, supporting scripts containing auxiliary functions for data pre- and post-processing, core.py, and a wrapper script to envelope the whole structure, run_energy_onshore.py.

Implemented indicators

Wind energy indicators:

Wind Speed Anomalies
Wind anomalies compared to a 30-year baseline reference period.
Wind Power Density (WPD)
Amount of energy available in wind at a given location.
Capacity Factor (CF)
Ratio of actual energy produced by a wind turbine compared to its maximum theoretical output.
Capacity Factor Histogram
Histogram of capacity factors over a 2D grid.
Capacity Factor Histogram (1D)
Histogram of capacity factors at a given location.
Wind Speed Histogram (now comes from the OPA)
Histogram of wind speed over a 2D grid.
Wind Speed Histogram (1D) (now comes from the OPA)
Histogram of wind speed at a given location.
Annual Energy Production (AEP)
Energy produced by a wind turbine / wind farm over a year.
High Wind Events
Number of times wind speed exceeds a given threshold.
Low Wind Events
Number of times wind speed is below a given threshold.
Cooling Degree Days (CDD)
Weather-based index designed to describe the energy requirements of buildings in terms of cooling.
Heating Degree Days (HDD)
Weather-based index designed to describe the energy requirements of buildings in terms of heating.

Wind speed anomalies: wind_speed_anomalies(ws, climatology, scale='daily')
Compute the wind speed anomalies.
- Input:
  - ws: xarray.DataArray ; (time,lat,lon) -> Wind speed magnitude.
  - climatology: xarray.DataArray ; (time,lat,lon) -> Climatology of wind speed over a 30-year reference period.
  - scale: str -> Temporal scale of the climatology. Options are 'daily', 'monthly' and 'yearly'. (default: 'daily')
- Output:
  - ws_anom: xarray.DataArray ; (time,lat,lon) -> Wind speed anomalies.
- References
  [1]:
Wind power density: wind_power_density(ws, air_density=1.225)
Compute the wind power density.
- Input:
  - ws: xarray.DataArray ; (time,lat,lon) -> Wind speed magnitude.
  - air_density: float -> Air density. Default value is 1.225 kg m^(-3).
- Output:
  - wpd: xarray.DataArray ; (time,lat,lon) -> Wind power density.
- References
  [1]:
Capacity factor: capacity_factor(ws, iec_class)
Compute the capacity factor of a wind turbine.
- Input:
  - ws: xarray.DataArray ; (time,lat,lon) -> Wind speed magnitude at hub height.
  - iec_class: str -> IEC wind turbine class. Options are 'I','I/II', 'II', 'II/III', 'III', 'S'.
- Output:
  - cf: xarray.DataArray ; (time,lat,lon) -> Capacity factor.
- References
  [1]: https://doi.org/10.1016/j.renene.2019.04.135
Capacity factor histogram: capacity_factor_histogram(ws, bins, iec_class)
Compute the capacity factor histogram of a wind turbine over a 2D grid.
- Input:
  - ws: xarray.DataArray ; (time,lat,lon) -> Wind speed magnitude at hub height.
  - bins: int -> Number of bins.
  - iec_class: str -> IEC wind turbine class. Options are 'I','I/II', 'II', 'II/III', 'III', 'S'.
- Output:
  - counts: xarray.DataArray ; (bins,lat,lon) -> Number of counts in each bin.
  - bin_edges: xarray.DataArray ; (bins+1,lat,lon) -> Bin edges.
- References
  [1]: https://doi.org/10.1016/j.renene.2019.04.135
Capacity factor histogram (1D): capacity_factor_histogram_1D(ws, bins, target_lon, target_lat, iec_class)
Compute the capacity factor histogram of a wind turbine at a given location.
- Input:
  - ws: xarray.DataArray ; (time,lat,lon) -> Wind speed magnitude at hub height.
  - bins: int -> Number of bins.
  - target_lon: float -> Longitude of the target location.
  - target_lat: float -> Latitude of the target location.
  - iec_class: str -> IEC wind turbine class. Options are 'I','I/II', 'II', 'II/III', 'III', 'S'.
- Output:
  - counts: xarray.DataArray ; (bins) -> Number of counts in each bin.
  - bin_edges: xarray.DataArray ; (bins+1) -> Bin edges.
- References
  [1]: https://doi.org/10.1016/j.renene.2019.04.135
Wind speed histogram: wind_speed_histogram(ws, bins)
Compute the wind speed histogram over a 2D grid.
- Input:
  - ws: xarray.DataArray ; (time,lat,lon) -> Wind speed magnitude at hub height.
  - bins: int -> Number of bins.
- Output:
  - counts: xarray.DataArray ; (bins,lat,lon) -> Number of counts in each bin.
  - bin_edges: xarray.DataArray ; (bins+1,lat,lon) -> Bin edges.
- References
  [1]: https://numpy.org/doc/stable/reference/generated/numpy.apply_along_axis.html
  [2]: https://numpy.org/doc/stable/reference/generated/numpy.histogram.html
Wind speed histogram (1D): wind_speed_histogram_1D(ws, bins, target_lon, target_lat)
Compute the wind speed histogram at a given location.
- Input:
  - ws: xarray.DataArray ; (time,lat,lon) -> Wind speed magnitude at hub height.
  - bins: int -> Number of bins.
  - target_lon: float -> Longitude of the target location.
  - target_lat: float -> Latitude of the target location.
- Output:
  - `counts: xarray.DataArray ; (bins) -> Number of counts in each bin.
  - `bin_edges: xarray.DataArray ; (bins+1) -> Bin edges.
- References
  [1]: https://numpy.org/doc/stable/reference/generated/numpy.histogram.html
Annual energy production: annual_energy_production_wind(capacity_factor, rated_power, num_turbines=1)
Compute the annual energy production of a wind turbine from its capacity factor time series.
- Input:
  - capacity_factor: xarray.DataArray ; (time) -> Capacity factor.
  - rated_power: float / int -> Rated power of the wind turbine in MW.
  - num_turbines: int -> Number of wind turbines. (default: 1)
- Output:
  - aep: xarray.DataArray ; (time) -> Annual energy production in MWh.
- References
  [1]: https://doi.org/10.1016/j.renene.2019.04.135
High wind events: high_wind_events(ws, threshold=25.0)
Compute where and when wind speed exceeds a given threshold (cut-out speed).
- Input:
  - ws: xarray.DataArray ; (time,lat,lon) -> Wind speed magnitude at hub height.
  - threshold: float -> Wind speed threshold (default: 25.0 m/s). Cut-out speed of the wind turbine.
- Output:
  - hwe: xarray.DataArray ; (lat,lon) -> Number of high wind events.
Low wind events: low_wind_events(ws, threshold=3.0)
Compute where and when wind speed is below a given threshold (cut-in speed).
- Input:
  - ws: xarray.DataArray ; (time,lat,lon) -> Wind speed magnitude at hub height.
  - threshold: float -> Wind speed threshold (default: 3.0 m/s). Cut-in speed of the wind turbine.
- Output:
  - lwe: xarray.DataArray ; (lat,lon) -> Number of low wind events.
Cooling degree days: cooling_degree_days(tm, tx, tn, base=22.0)
Compute the average cooling degree days. Requires daily mean, maximum and minimum temperature.
- Input:
  - tm: xarray.DataArray ; (time,lat,lon) -> Mean temperature.
  - tx: xarray.DataArray ; (time,lat,lon) -> Maximum temperature.
  - tn: xarray.DataArray ; (time,lat,lon) -> Minimum temperature.
  - base: float -> Base temperature (default: 22.0°C). Depends on the region/country considered.
- Output:
  - cdd: xarray.DataArray ; (time,lat,lon) -> Cooling degree days.
  - cdd_acc: xarray.DataArray ; (lat,lon) -> Total accumulated cooling degree days.
- References
  [1]: https://doi.org/10.1002/joc.3959
Heating degree days: heating_degree_days(tm, tx, tn, base=15.5)
Compute the average heating degree days. Requires daily mean, maximum and minimum temperature.
- Input:
  - tm: xarray.DataArray ; (time,lat,lon) -> Mean temperature.
  - tx: xarray.DataArray ; (time,lat,lon) -> Maximum temperature.
  - tn: xarray.DataArray ; (time,lat,lon) -> Minimum temperature.
  - base: float -> Base temperature (default: 15.5°C). Depends on the region/country considered.
- Output:
  - hdd: xarray.DataArray ; (time,lat,lon) -> Heating degree days.
  - hdd_acc: xarray.DataArray ; (lat,lon) -> Total accumulated heating degree days.
- References
  [1]: https://doi.org/10.1002/joc.3959

Solar energy indicators:

Solar Capacity Factor (daily)
Capacity factor of a PV solar panel at daily scale.
Annual Energy Production (daily)
Annual energy production of a PV solar panel at daily scale.

Solar Capacity Factor (daily): solar_capacity_factor_daily(t2c, rsds)
Compute the capacity factor of a PV solar panel at daily scale.
- Input:
  - t2c: xarray.DataArray ; (time,lat,lon) -> Daily temperature at 2m in °C.
  - rsds: xarray.DataArray ; (time,lat,lon) -> Daily surface solar radiation donwnwards in W m^(-2).
- Output:
  - cf_daily: xarray.DataArray ; (time,lat,lon) -> Daily capacity factor.
- References
  [1]: https://doi.org/10.1016/j.renene.2015.10.006 \ [2]: https://doi.org/10.1038/ncomms10014
Annual Energy Production (daily): annual_energy_production_daily(capacity_factor, rated_power, num_panels=1)
Compute the annual energy production of a PV solar panel.
- Input:
  - capacity_factor: xarray.DataArray ; (time) -> Capacity factor time series for a year.
  - rated_power: float / int -> Rated power of the solar panel in kW.
  - num_panels: int -> Number of solar panels. (default: 1)
- Output:
  - aep_daily: xarray.DataArray ; (time) -> Annual energy production in kWh.

Version

Current version can be found at the latest publised tags in the git information.

How to run

Each function/indicator includes a description of its aim, inputs, outputs and corresponding references. The following is an example of how to run the capacity_factor function in a Jupyter Notebook / Python environment:

import xarray as xr
from energy_onshore.core import wind_speed
from energy_onshore import capacity_factor

# Load wind speed data
path_to_data = 'path/to/data/'
data = xr.open_dataset(path_to_data + 'data.nc')
data.close()

u100 = data['u']
v100 = data['v']

# Compute wind speed
ws = wind_speed(u100, v100)

# Compute capacity factor
cf = capacity_factor(ws, iec_class='I')

# Save capacity factor to netCDF
path_to_output = 'path/to/output/'
cf.to_netcdf(path_to_output + 'capacity_factor.nc')

The same procedure can be followed for the rest of the indicators, adjusting the input data and parameters accordingly.

Input & Processing

The different indicators are built to work with data array objects from the xarray library: xarray.DataArray

Several support functions have been implemented to facilitate the pre-processing of input data. These functions are included in core.py

check_temperature(data) Check if temperature is in Kelvin or Celsius.
- Input:
  - data: xarray.Dataset / xarray.DataArray -> Temperature data.
- Output:
  - unit: str -> Temperature unit of the data. Possible values are 'C' (Celsius) and 'K' (Kelvin).
convert_temperature(t, unit='C') Convert temperature from Kelvin to Celsius or from Celsius to Kelvin.
- Input:
  - t: xarray.DataArray ; (time,lat,lon) -> Temperature.
  - unit: str -> Unit to convert to (default: 'C').
- Output:
  - t_conv: xarray.DataArray ; (time,lat,lon) -> Converted temperature.
wind_speed(u, v) Compute wind speed magnitude from u and v components.
- Input:
  - u: xarray.DataArray ; (time,lat,lon) -> U-component of wind.
  - v: xarray.DataArray ; (time,lat,lon) -> V-component of wind.
- Output:
  - ws: xarray.DataArray ; (time,lat,lon) -> Wind speed magnitude.
wind_direction(u, v) Compute wind direction from u and v components.
- Input:
  - u: xarray.DataArray ; (time,lat,lon) -> U-component of wind.
  - v: xarray.DataArray ; (time,lat,lon) -> V-component of wind.
- Output:
  - wd: xarray.DataArray ; (time,lat,lon) -> Wind direction.
cosine_sza_hourly(start_date, end_date, lats, lons) Computes the cosine of the Solar Zenith Angle (SZA).
- Input:
  - start_date: numpy.datetime64 -> Start date for the time period of interest.
  - end_date: numpy.datetime64 -> End date for the time period of interest.
  - lats: numpy.ndarray -> Latitude values.
  - lons: numpy.ndarray -> Longitude values.
- Output:
  - cossza: xarray.DataArray ; (time,lat,lon) -> Cosine of the Solar Zenith Angle.
percentile(var, percentile, axis=0) Compute percentile of a variable along a given axis (i.e. dimension).
- Input:
  - var: xarray.DataArray ; (time,lat,lon) -> Variable.
  - percentile: float -> Percentile to compute (0 to 100).
  - axis: int -> Axis along which to compute the percentile. Default is 0 (time dimension).
- Output:
  - perc: xarray.DataArray ; (lat,lon) -> Percentile of the variable.
moving_average(data, window_size) Compute the moving average of a variable for the previous 'window_size' time steps.
- Input:
  - data: xarray.DataArray ; (time,lat,lon) -> Variable.
  - window_size: int -> Window size of the moving average.
- Output:
  - avg: xarray.DataArray ; (time,lat,lon) -> Moving average of the variable.
temporal_rescaling(var, scale='None') Rescale a variable to a different temporal frequency.
- Input:
  - var: xarray.DataArray ; (time,lat,lon) -> Variable.
  - scale: str -> Temporal frequency to which the variable is rescaled. Possible values are 'daily', 'weekly', 'monthly', 'annual', and 'seasonal'. Default is 'None'.
- Output:
  - out: xarray.DataArray ; (time,lat,lon) -> Rescaled variable.
select_point(data, target_lon, target_lat) Select the closest point from a data array based on a longitude and latitude of interest.
- Input:
  - data: xarray.DataArray ; (time,lat,lon) -> Data array from which the point is selected.
  - target_lon: float -> Longitude of interest.
  - target_lat: float -> Latitude of interest.
- Output:
  - out: xarray.DataArray ; (time) -> Data array at closest point.
select_region(data, latbox, lonbox) Select a rectangular region from a DataArray based on a longitude and latitude box.
- Input:
  - data: xarray.DataArray ; (time,lat,lon) -> Data array from which the region is selected.
  - lonbox: tuple / list -> Longitude box (min, max).
  - latbox: tuple / list -> Latitude box (min, max).
- Output:
  - out: xarray.DataArray ; (time,lat,lon) -> Data array at selected region.
create_dataset(vars, attrs, coords, dims) Creates an xarray dataset with the specified variables, coordinates, dimensions and attributes. Inputs are provided as dictionaries.
- Input:
  - vars: dict -> A dictionary where keys are the variable names and values are numpy arrays or lists.
  - attrs: dict -> A dictionary where keys are the variable names and values are numpy arrays or lists.
  - coords: dict -> A dictionary where keys are the coordinate names and values are numpy arrays or lists.
  - dims: tuple -> A tuple of dimension names in the order they should appear in the dataset.
- Output:
  - ds: xarray.Dataset -> An xarray Dataset containing the specified variables, attributes, coordinates and dimensions.
get_type(x) Return the type of the variable in string format. Checks if variable is of the correct type.
- Input:
  - x: any -> Variable to check. 'x' can be of any type.
- Output:
  - type: str -> Variable type in string format.
check_dims(data, n_dims) Check if the number of dimensions of a xarray data structure is correct.
- Input:
  - data: xarray.Dataset / xarray.DataArray / list of xarray.Dataset / list of xarray.DataArray -> Data structure to check.
  - n_dims: int -> Number of dimensions. Default is 3.
- Output: If the number of dimensions is correct, return None. Otherwise, raise an error.

Output & Visualization

The output of the functions follows the same xarray.DataArray structure and can be directly stored in netCDF format. As of now, the package does not include a visualization module, but it is planned to be implemented in future versions.

Roadmap

Next versions will include a visualization module to plot the results of the different indicators.

Dependencies

xarray
numpy
scipy
pandas
datetime

Support

For any feedback, comments and/or issues you can contact me through Gitlab or directly by email at [email protected]

To install the necessary dependencies for the package:

pip install git+https://earth.bsc.es/gitlab/digital-twins/de_340/energy_onshore.git@main

To copy the repository to your local directory:

git clone https://earth.bsc.es/gitlab/digital-twins/de_340/energy_onshore.git

To install the package locally from the root directory (where the setup.py file is located):

pip install .

To check the version of the package in Python:

>>> import energy_onshore
>>> energy_onshore.__version__

If you are installing the demonstrator version from Github:

pip install git+https://github.com/DestinE-Climate-DT/energy_onshore_demonstrator.git@main

git clone https://github.com/DestinE-Climate-DT/energy_onshore_demonstrator.git

How to test:

Clone the repository

git clone https://earth.bsc.es/gitlab/digital-twins/de_340-2/energy_onshore.git

Create a virtual environment

python -m venv venv

activate environment

source venv/bin/activate

pip install in edit mode -e and with the testing dependencies [test]

pip install -e .[test]

Run pytest

pytest .

Advanced: use pytest to debug tests.

pytest --pdb

How to crete the stac catalog from the output:

Non-DestinE catalog:

create a data directory, where you store your output.

mkdir data

run the generate_stac_catalog.py sctipt (one dir above data).

python3 generate_stac_catalog.py

Catalog to be transferred to the Eumetsat Datalake:

Follow these instructions https://destine-data-lake-docs.data.destination-earth.eu/en/latest/dedl-discovery-and-data-access/User-Generated-Data/Promote-user-data-to-become-DestinE-data/Promote-user-data-to-become-DestinE-data.html#step-4-data-preparation

Liscence

Copyright 2022-2025 European Union (represented by the European Commission) The Energy Indicators package is distributed as open-source software under Apache 2.0 License. The copyright owner is the European Union, represented by the European Commission. The development of the Energy Indicators package has been funded by the European Union through Contract DE_340_CSC - Destination Earth Programme Climate Adaptation Digital Twin (Climate DT). Further info can be found at https://destine.ecmwf.int/ and https://destination-earth.eu/

Name		Name	Last commit message	Last commit date
Latest commit History 3 Commits
docs/source		docs/source
energy_onshore		energy_onshore
run		run
test_data		test_data
tests		tests
utils		utils
LICENSE		LICENSE
README.md		README.md
changelog.md		changelog.md
dummy		dummy
generate_stac_metadata.py		generate_stac_metadata.py
pyproject.toml		pyproject.toml
pytest.ini		pytest.ini
setup.py		setup.py

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Energy Indicators (formely Energy Onshore)

Description

Implemented indicators

Wind energy indicators:

Solar energy indicators:

Version

How to run

Input & Processing

Output & Visualization

Roadmap

Dependencies

Support

How to test:

How to crete the stac catalog from the output:

Non-DestinE catalog:

Catalog to be transferred to the Eumetsat Datalake:

Liscence

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Energy Indicators (formely Energy Onshore)

Description

Implemented indicators

Wind energy indicators:

Solar energy indicators:

Version

How to run

Input & Processing

Output & Visualization

Roadmap

Dependencies

Support

How to test:

How to crete the stac catalog from the output:

Non-DestinE catalog:

Catalog to be transferred to the Eumetsat Datalake:

Liscence

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