hEFTy

Some (relatively) lightweight short-term energy forecasting tools for solar, wind, and load.

This repository currently includes solar and wind tools, but may expand one day to include electric load. Forecasts can be created using the NOAA GFS, NOAA GEFS, NOAA HRRR, and ECMWF IFS and AIFS (open data versions) models. The ECMWF CAMS version of IFS is also included, but only via hefty.solar.get_solar_forecast(), and it requires cdsapi to be installed and the user needs an API key (see https://ads.atmosphere.copernicus.eu/how-to-api).

For solar, look at the notebook solar_example.ipynb for some examples, and more_solar_examples.ipynb for more examples. Both of these convert the resource forecasts to power.

There are also solar ensemble forecasts demonstrated in ensemble_example.ipynb.

For wind, look at the notebook wind_example.ipynb. The wind tools are not as developed at the solar tools.

The custom.py module is intended to help with getting forecasts of "custom" weather parameters, not necessarily specific to wind or solar, which migh be useful for load forecasting.

Quick examples

Here's a quick example of getting a solar resource data forecast:

from hefty.solar import get_solar_forecast

latitude = 33.5
longitude = -86.8
init_date = '2024-06-05 6:00' # datetime the forecast model was initialized
resource_data = get_solar_forecast(
    latitude,
    longitude,
    init_date,
    run_length=18, # 18 hours are included in the forecast
    lead_time_to_start=3, # forecast starts 3 hours out from the init_date
    model='hrrr', # use NOAA HRRR
)
resource_data[
    ['ghi','dni','dhi','temp_air','wind_speed']
              ].plot(drawstyle='steps-mid')

with this output:

<img src="images/output.png" width="500"/>

Here's a wind resource forecast:

from hefty.wind import get_wind_forecast

latitude = 33.5
longitude = -86.8
init_date = '2024-06-05 6:00' # datetime the forecast model was initialized
resource_data = get_wind_forecast(
    latitude,
    longitude,
    init_date,
    run_length=18, # 18 hours are included in the forecast
    lead_time_to_start=3, # forecast starts 3 hours out from the init_date
    model='gfs', # use NOAA GFS
)
resource_data[
    ['wind_speed_10m', 'wind_speed_80m',
    'wind_speed_100m', 'temp_air_2m', 
    'pressure_0m']
    ].plot(secondary_y=['pressure_0m'], drawstyle='steps-mid')

with this output (note that pressure is on the secondary y-axis):

<img src="images/output_wind.png" width="500"/>

Installation

A virtual environment is strongly recommended. You can install from PyPi with:

pip install hefty

To run the example Jupyter notebooks, you also need jupyter:

pip install jupyter

If you want to use ECMWF CAMS, you also need cdsapi:

pip install cdsapi

References

This project uses several Python packages, including pvlib, an open-source solar PV modeling package [1, 2], and Herbie [3, 4], a package for accessing weather forecast data from NOAA. pv_model.py (with the model_pv_power() function used here) comes from [5] which leverages some functions from [6].

<img src="images/pvlib_powered_logo_horiz.png" width="200"/>

[1] Anderson, K., Hansen, C., Holmgren, W., Jensen, A., Mikofski, M., and Driesse, A. “pvlib python: 2023 project update.” Journal of Open Source Software, 8(92), 5994, (2023). DOI: 10.21105/joss.05994.

[2] https://github.com/pvlib/pvlib-python

[3] Blaylock, B. K. (2025). Herbie: Retrieve Numerical Weather Prediction Model Data (Version 2025.3.1) [Computer software]. https://doi.org/10.5281/zenodo.4567540

[4] https://github.com/blaylockbk/Herbie

[5] https://github.com/williamhobbs/pv-system-model

[6] Hobbs, W., Anderson, K., Mikofski, M., and Ghiz, M. "An approach to modeling linear and non-linear self-shading losses with pvlib." 2024 PV Performance Modeling Collaborative (PVPMC). https://github.com/williamhobbs/2024_pvpmc_self_shade