📚 awesome-energy-models

A catalogue and categorization of open-source, Python-based energy models for energy system planning and operations.

Table of Contents

• What is this page about?
• How to Contribute?
• Energy Model Categorization
• Energy Models
• General Tools

What is this page about?

This page is an attempt to catalogize and categorize open-source python-based models for planning and operations of energy systems. The focus is on frameworks, libraries (frameworks vs libraries) and tutorials, over codes implementing specific energy model (e.g. accompanying a research paper). Even though projects with specific energy model implementations are included given the code quality is high.

Note: Some things that are out of scope for this page include: IoT/connectivity projects, web/data scrapers, visualization tools and data models/ontologies.

How to Contribute?

1. Add a model to list
2. Update categorization and links

Energy Model Categorization

The energy models are categorized according to several characteristics: code type, problem type, model type, energy assets included and scale. Here is an outline of all the characteristics and their meaning:

Characteristics	Fields
Code type	🧩 Framework: Defines concepts and components that are used to create a solution 📚 Library: Functions and classes to solve a specific task, e.g. generating demand profiles. 🎓 Tutorial: Educational walkthroughs of specific topics and concepts. 📄 Code: Implementation of specific energy model.
Problem type	🎬 Operations: Operations problem such as dispatch or continuous trading. 📝 Planning: Planning problems such as optimal investments or system configuration.
Model type	🎮 Simulation: Physical or behaviour simulations, typically allowing for "what-if"-analysis. 📈 Prediction: Forecasting and scenario generation. ⚙️ Optimization: Models performing decision optimization. 🤖 Agents: Models interacting with and learning from environments.
Energy assets	☀️ Solar: Solar PV and solar thermal assets. 🌬️ Wind: Wind turbines and wind farms. 🔋 Battery: Electrochemical batteries. 💦 Hydro: Electrochemical batteries. 💦 Heat pump: ⚡ Power grid:
Scale	⚡ Power transmission: Scale of power transmission and countries. 🏭 Power plant: Front-of-meter power plant scale. 🏙️ City/Region: City or region scale. 🏘️ Community: Energy community scale. 🏠 House/building: House or building scale. 🔋 Asset: Asset scale.

Energy Models (Frameworks and libraries)

In alphabetical order.

Name	Description	Problem Type	Model Type	Energy Assets	Links
alite	Calculate renewable power potentials and time series.	📝	🎮	☀️ 🌬️	[code], [docs], [pypi], [website]
boptest	Simulate and benchmark building heat transfer.	🎬	⚙️ 🎮	🏠	[code]
calliope	A multi-scale energy systems modelling framework.	📝	🎮	🔋 🏭 ⚡ ☀️ 🌬️	[code], [docs], [pypi], [website]
demandlib	Generate electricity and heating demand profiles.	📝	🎮	🏠	[code], [docs], [pypi]
Grid2Op	A framework to model sequential decision making in power grid operations.	🎬	🤖 ⚙️ 📈 🎮	⚡	[code], [docs], [pypi], [website]
HOPP	Assess optimal designs for the deployment of utility-scale hybrid energy plants.	📝	🎮	🔋 ☀️ 🌬️	[code], [docs], [pypi]
Hydropowerlib	Calculate feed-in time series of run-of-the-river hydropower plants.	📝	🎮	💦	[code]
OpenSTEF	Open energy forecasting package.	🎬	📈	☀️ 🌬️	[code], [docs], [pypi]
OSESMO	Calculate power flow of distribution grids.	📝	🎮	⚡	[code]
pandapipes	Calculate fluid flow of pipe systems.	📝	🎮	♨️	[code], [docs], [pypi], [website]
pandapower	Calculate power flow of distribution grids.	📝	🎮	⚡	[code], [docs], [pypi]
proloaf	Probabilistic electricity load forecasting using recurrent neural networks.	🎬	📈	⚡	[code], [pypi]
pvlib	Functions for simulating and forecasting photovoltaic energy systems.	📝	📈 🎮	☀️	[code], [docs], [pypi]
pyCity	Python package for data handling and scenario generation of city districts.	📝	🎮	🔋 🏠 ☀️ 🌬️	[code], [pypi]
PYPOWER	Power flow and Optimal Power Flow (OPF) solver. Port from MATPOWER to Python.	📝	🎮	⚡	[code], [docs], [pypi]
pysam	Python Wrapper for the System Advisor Model (SAM).	📝	🎮	🔋 🏠 ☀️ 🌬️	[code], [docs], [pypi]
TEASER	Tool for Energy Analysis and Simulation for Efficient Retrofit.	📝	🎮	🏠	[code], [pypi]
windpowerlib	Calculate wind power production from meteorological variables.	📝	📈 🎮	🌬️	[code], [docs], [pypi], [website]

Tutorials and examples

General tools

General tools are frameworks and libraries that are not necessarily energy models but that can be useful for implementation of specific energy models. We categorize these tools into:

• 🎮 Physics or data driven simulation tools.
• 📈 Statistical and machine learning prediction tools.
• ☀️ Optimization tools.
• 🤓 Explainability and interpretability tools.

Name	Description	Tool type	Links
autogluon	Fast and Accurate ML in 3 Lines of Code.	📈	[code], [docs], [PyPI]
catboost	A fast gradient boosting library with special treatment of categorical features.	📈	[code], [docs], [PyPI]
cvxopt	Framework for convex optimization in python.	⚙️	[code], [docs], [PyPI]
cvxpy	Framework for convex optimization in python.	⚙️	[code], [docs], [PyPI]
darts	A fast gradient boosting library for tasks including regression/prediction.	📈	[code], [docs], [PyPI]
gluon	A fast gradient boosting library for tasks including regression/prediction.	📈	[code], [docs], [PyPI]
lightgbm	A fast gradient boosting library for tasks including regression/prediction.	📈	[code], [docs], [PyPI]
pycaret	An open-source, low-code machine learning library in Python.	⚙️	[code], [docs], [PyPI]
pyomo	An object-oriented algebraic modeling language for structured optimization problems.	⚙️	[code], [docs], [PyPI]
scikit-learn	The go-to machine learning library for Python.	📈	[code], [docs], [PyPI]
shap	A game theoretic approach to explain the output of any machine learning model.	📈	[code], [docs], [PyPI]
rsome	Robust Stochastic Optimization Made Easy.	⚙️	[code], [docs], [PyPI]
xgboost	Extreme gradient boosting algorithm for tasks including regression/prediction.	📈	[code], [docs], [PyPI]