WAVES: Wind Asset Value Estimation System

Overview

Runs analyses for offshore wind projects by utilizing ORBIT (CapEx), WOMBAT (OpEx), and FLORIS (AEP) to estimate the lifecycle costs using NREL's flagship technoeconomic models.

Please visit our documentation site for API documentation, a reference guide, and examples.

Requirements

Python 3.10+

Environment Setup

Download the latest version of Miniconda for the appropriate OS. Follow the remaining steps for the appropriate OS version.

Using conda, create a new virtual environment and replace "waves" with a different environment name, if preferred:

conda create -n waves python=3.14
conda activate waves
conda install -c anaconda pip
conda config --set pip_interop_enabled true

# to deactivate
conda deactivate

Installation

Requires Python 3.10+.

For basic usage, users can install WAVES directly from PyPI, or from source for more advanced usage.

Pip

pip install waves

From Source

A source installation is great for users that want to work with the provided example, and potentially modify the code at a later point in time.

git clone https://github.com/NREL/WAVES.git
cd WAVES
pip install .

If working with the example, or running with Jupyter Notebooks, be sure to install the examples dependencies like the following:

pip install ".[examples]"

Tinkering

Use the -e for an editable installation, in case you plan on editing any underlying code.

pip install -e .

Usage

After installation, the package can imported:

python
import waves
waves.__version__

CLI

waves library-path configuration1.yaml configuration2.yaml

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

waves-0.7.1.tar.gz (66.8 MB view details)

Uploaded Aug 17, 2026 Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

The dropdown lists show the available interpreters, ABIs, and platforms. Enable javascript to be able to filter the list of wheel files.

waves-0.7.1-py3-none-any.whl (43.0 kB view details)

Uploaded Aug 17, 2026 Python 3

File details

Details for the file waves-0.7.1.tar.gz.

File metadata

Download URL: waves-0.7.1.tar.gz
Upload date: Aug 17, 2026
Size: 66.8 MB
Tags: Source
Uploaded using Trusted Publishing? Yes
Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for waves-0.7.1.tar.gz
Algorithm	Hash digest
SHA256	`bd9971824495a235dd8c159a38c129a6ac3ba4ef8781049471327da67ad6edd9`
MD5	`0b5555c177e3e559700fc654e11b2c4c`
BLAKE2b-256	`bd1949f87e5fa514d892080ea7f5fd88aa2d7440d31bb32b593190f0abcdf50c`

See more details on using hashes here.

Provenance

The following attestation bundles were made for waves-0.7.1.tar.gz:

Publisher: python-publish.yml on NatLabRockies/WAVES

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Statement:
- Statement type: https://in-toto.io/Statement/v1
- Predicate type: https://docs.pypi.org/attestations/publish/v1
- Subject name: waves-0.7.1.tar.gz
- Subject digest: bd9971824495a235dd8c159a38c129a6ac3ba4ef8781049471327da67ad6edd9
- Sigstore transparency entry: 2499690415
- Sigstore integration time: Aug 17, 2026
Source repository:
- Permalink: NatLabRockies/WAVES@cc843037e5f1d2a2513ecfbef9e6cd93a17fcc30
- Branch / Tag: refs/tags/v0.7.1
- Owner: https://github.com/NatLabRockies
- Access: public
Publication detail:
- Token Issuer: https://token.actions.githubusercontent.com
- Runner Environment: github-hosted
- Publication workflow: python-publish.yml@cc843037e5f1d2a2513ecfbef9e6cd93a17fcc30
- Trigger Event: release