elharrab's Stars
lukas-blecher/LaTeX-OCR
pix2tex: Using a ViT to convert images of equations into LaTeX code.
MorvanZhou/Reinforcement-learning-with-tensorflow
Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学
protontypes/open-sustainable-technology
A directory and analysis of the open source ecosystem in the areas of climate change, sustainable energy, biodiversity and natural resources. https://docs.getgrist.com/gSscJkc5Rb1R/OpenSustaintech
jrieke/traingenerator
🧙 A web app to generate template code for machine learning
PacktPublishing/Deep-Reinforcement-Learning-Hands-On-Second-Edition
Deep-Reinforcement-Learning-Hands-On-Second-Edition, published by Packt
chimbori/google-calendar-crx
Google Calendar for Chrome
AutoViML/Auto_TS
Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Created by Ram Seshadri. Collaborators welcome.
omerbsezer/Reinforcement_learning_tutorial_with_demo
Reinforcement Learning Tutorial with Demo: DP (Policy and Value Iteration), Monte Carlo, TD Learning (SARSA, QLearning), Function Approximation, Policy Gradient, DQN, Imitation, Meta Learning, Papers, Courses, etc..
hubbs5/or-gym
Environments for OR and RL Research
Total-RD/pymgrid
pymgrid is a python library to generate and simulate a large number of microgrids.
robinhenry/gym-anm
A framework to design Reinforcement Learning environments that model Active Network Management (ANM) tasks in electricity distribution networks.
oemof/feedinlib
This repository contains implementations of photovoltaic models to calculate electricity generation from a pv installation based on given solar radiation. Furthermore it contains all necessary pre-calculations.
Pyomo/mpi-sppy
MPI-based Stochastic Programming in PYthon
oemof/demandlib
Creating heat and power demand profiles from annual values.
uber-research/MARVIN
Uber's Multi-Agent Routing Value Iteration Network
lingquant/msppy
linker81/Reinforcement-Learning-CheatSheet
Cheatsheet of Reinforcement Learning (Based on Sutton-Barto Book - 2nd Edition)
alefunxo/Basopra
BASOPRA - BAttery Schedule OPtimizer for Residential Applications. Daily battery schedule optimizer (i.e. 24 h optimization framework), assuming perfect day-ahead forecast of the electricity demand load and solar PV generation in order to determine the maximum economic potential regardless of the forecast strategy used. Include the use of different applications which residential batteries can perform from a consumer perspective. Applications such as avoidance of PV curtailment, demand load-shifting and demand peak shaving are considered along with the base application, PV self-consumption. Different battery technologies and sizes can be analyzed as well as different tariff structures. Aging is treated as an exogenous parameter, calculated on daily basis and is not subject of optimization. Data with 15-minute temporal resolution are used for simulations. The model objective function have two components, the energy-based and the power-based component, as the tariff structure depends on the applications considered, a boolean parameter activate the power-based factor of the bill when is necessary.
energy-modelling-toolkit/prosumpy
prosumpy – Energy prosumer analysis with Python
microsoft/microsoft-bonsai-api
A collection of libraries for interfacing simulators with the Bonsai platform.
erachelson/rlvs_rl_fundamentals
erykml/isolation_forest_example
Example of implementing Isolation Forest in Python
mathildebadoual/ev_controller
Markov Decision Process and Model Predictive control for EV charging station
alabatie/optim-pv-battery
Solution for DrivenData Challenge "Power Laws: Optimizing Demand-side Strategies" (using DQNs, policy networks)
cadema-PoliTO/RECOpt
The repository contains a routine that optimizes the operation of a PV system with energy storage for fixed or variable (parametric) sizes for both of them, in the context of collective self-consumption and energy communities in Italy. PV production data are to be provided by the user (PVGIS database can be used), while consumption profiles are generated for an aggregate of households using probabilistic methods.
ArciAndres/pev_battery_charge
Battery charge management environment, designed as a multi-agent scenario with continuous observation and action space, where the agents are charging stations that must meet the energy requirements of a previously-scheduled group of PEVs (Plug-in Electric Vehicles), constrained to a local power supply restriction, and a global restriction from the containing Load Area.
0-k/prosumerpolicy
Modeling the optimum dispatch of solar PV-battery systems under different policy instrument mixes.
rdnfn/solar-agent
This project provides Gym environments to train RL agents to operate batteries in solar-plus-battery installations.
scoldi/time-series-pattern-detection
Pattern recognition in an electrical signal.
TanguyLevent/pymgrid
pymgrid is a python library to generate and simulate a large number of microgrids.