PyPSA: Python for Power System Analysis
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Updated
May 16, 2024 - Python
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
Pathways for Renewable Energy Planning coupling Short-term Hydropower OperaTion
A versatile simulation and optimization platform for power-system planning and operations.
Quality control, filtering, feature labeling, and other tools for working with data from photovoltaic energy systems.
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
☀️ Open-source view-factor model for diffuse shading and bifacial PV modeling. Documentation:
Optimize energy assets using mixed-integer linear programming
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.
My master's dissertation on wind turbine fault prediction using machine learning
Access data, statistics, and visualizations for New York's electricity grid.
Dockerized Repo for "3D-PV-Locator: Large-scale detection of rooftop-mounted photovoltaic systems in 3D" based on Applied Energy publication.
Geospatial Land Availability for Energy Systems
python Generator of REnewable Time series and mAps
A workflow to build models of the European electricity system for Calliope.
ELM is a collection of utilities to apply Large Language Models (LLMs) to energy research.
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.
PyTorch models and pipeline developed for "DeepSolar for Germany". For reference, the paper can found at https://ieeexplore.ieee.org/document/9203258
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