Turns Data and AI algorithms into production-ready web applications in no time.
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Updated
Sep 7, 2024 - Python
Turns Data and AI algorithms into production-ready web applications in no time.
PyPSA-Earth: A flexible Python-based open optimisation model to study energy system futures around the world.
A Python library to build powerful and customized data-driven back-end applications.
TIMES-Ireland Model (TIM)
Source code for figure generation and analysis of the ENGAGE netzero scenario analysis
A data warehouse for high-powered scenario analysis in the domain of integrated assessment of climate change and energy systems modeling
Open model and data for SWITCH-China
FlyingKoala turns Excel into a user interface for defining calculations in Python and so reduces the complexity for humans translating from Excel to Python and makes the calculations more transparent to other domain experts, managers, bankers and auditors.
Tools for Stochastic Simulation using diffusion models (R).
The Connected and Automated Vehicles Scenario Generation (CSG) model is a system dynamics model. It was built by NREL and funded by the U.S. Department of Energy under the Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility initiative. The purpose of the model is to simulate the “transitions from predominantly indivi…
Model of the Italian energy system from 2006 to 2050, based on the TEMOA modeling framework and developed by MAHTEP Group.
Tutorial notebook of the pyam package
A library of published compartmental epidemic models, and classes to represent demographic structure, non-pharmaceutical interventions, and vaccination regimes, to compose epidemic scenarios.
🚧 🚔 ⚠ Toolbox to compute Criticality Measures for Automated Vehicles
Fan Chart for Economic Uncertainty
MetaScenario: A Framework for Driving Scenario Data Description, Storage and Indexing
Conception, architecture and first prototype for Bineco, a recycling app. This project was done in a software engineering course under Louis-Edouard LAFONTANT
Model of the Piedmont (Italian region) energy system from 2011 to 2050, based on the TEMOA modeling framework and developed by MAHTEP Group.
Problem Statements: Analyze historical sales and profit and territory. Identify bestselling products and attributed customers and regions. Forecast revenue for the next 7 periods. Execute pricing scenario analysis to understand increase in product cost.
In this repository, a python script is used to perform the consistency analysis of the scenario analysis according to von Reibnitz (1992).
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