Extract data from ISOs and other energy grid sources
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Updated
Jul 2, 2026 - Python
Extract data from ISOs and other energy grid sources
City Energy Analyst (CEA) is an open-source urban building energy modeling (UBEM) platform and computation tool for the design of low-carbon and highly efficient cities.
Tools for producing high-quality hourly generation and emissions data for U.S. electric grids
Demand Response Analysis Framework (DRAF)
This is a repository for cloud adoption tutorials oriented to power system practitioners
Co-optimization model of power systems and hydrogen systems with option to run MGA
Interactive Streamlit app to evaluate ship fuel mixes against FuelEU Maritime targets, model EU ETS coverage/phase-in and costs, explore mitigation (pooling, bio/RFNBO, replacement), visualize trends, and export configurable PDF reports.
Northwestern University Freight Rail Infrastructure & Energy Network Decarbonization (NUFRIEND) Framework
Backend source code to produce geospatial data layers for the MCSC's Geospatial Trucking Industry Decarbonization Explorer (Geo-TIDE)
A python version of the NYgrid model.
Capacity expansion model of different U.S. Power systems (Eastern Interconnection, Western Interconnection, ERCOT)
Decision-support tool comparing CCS retrofits vs. electrification for hard-to-abate industries (steel, cement, chemicals) on cost, abatement, jobs, and timeline. Built for the Laidlaw Scholars Industrial Transitions project.
A multi-basin, multi-vessel, instance-matched benchmark for ship weather-routing and speed-optimization research (96 instances x 3 hulls, ERA5 weather, first-principles physics).
A carbon-intensity data API: live gCO₂/kWh for cloud regions from real grid-operator sources, plus carbon-aware region routing and GHG-Protocol compliance reporting.
Python package for calculating Heating/Cooling Degree Days to model energy consumption.
Practicality of Green H2 Economy for Industry and Maritime Sector Decarbonization through Multi-objective Optimization and RNN-LSTM Model Analysis
Accurate short-term load forecasting is critical for decarbonizing buildings, which contribute nearly one-third of global energy use and emissions.
Interactive Streamlit dashboard for Mexico's GHG decarbonization scenario analysis (2020–2050). Four scenarios × 8 sectors with Monte Carlo uncertainty bands and DMDU robustness table.
Technoeconomic assessment of low-emission steelmaking using hydrogen plasma.
An enterprise-grade, interactive industrial decarbonization platform developed by Srinivasa. Powered by Streamlit and Scikit-Learn, this application implements a physics-enforced machine learning pipeline to track, optimize, and reduce manufacturing carbon footprints. Features real-time SCADA telemetry simulation, automated closed-loop anomaly dete
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