PyPSA: Python for Power System Analysis

A set of documented functions for simulating the performance of photovoltaic energy systems.

atlite: A Lightweight Python Package for Calculating Renewable Power Potentials and Time Series

Quality control, filtering, feature labeling, and other tools for working with data from photovoltaic energy systems.

A versatile simulation and optimization platform for power-system planning and operations.

The model for the REopt API, which is used as the back-end for the REopt Webtool (reopt.nrel.gov/tool), and can be accessed directly via the NREL Developer Network (https://developer.nrel.gov/docs/energy-optimization/reopt)

Toolkit for working with RADIANCE for the ray-trace modeling of Bifacial Photovoltaics

The Super-Resolution for Renewable Resource Data (sup3r) software uses generative adversarial networks to create synthetic high-resolution wind and solar spatiotemporal data from coarse low-resolution inputs.

Optimize energy assets using mixed-integer linear programming

☀️ Open-source view-factor model for diffuse shading and bifacial PV modeling. Documentation:

My master's dissertation on wind turbine fault prediction using machine learning

Geospatial Land Availability for Energy Systems

Dockerized Repo for "3D-PV-Locator: Large-scale detection of rooftop-mounted photovoltaic systems in 3D" based on Applied Energy publication.

Wind Power Forecasting Based on Hybrid CEEMDAN-EWT Deep Learning Method

ELM is a collection of utilities to apply Large Language Models (LLMs) to energy research.

python Generator of REnewable Time series and mAps

A workflow to build models of the European electricity system for Calliope.

Using an integrated pinball-loss objective function in various recurrent based deep learning architectures made with keras to simultaneously produce probabilistic forecasts for UK wind, solar, demand and price forecasts.

Energinets Model Testbench. Automate gridcompliance studies in PSCAD and Powerfactory.

PyTorch models and pipeline developed for "DeepSolar for Germany". For reference, the paper can found at https://ieeexplore.ieee.org/document/9203258