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activsg200r

A revised ACTIVSg200 synthetic grid (Illinois system) for Unit Commitment

ai_elevator_challenge

an-adaptive-hierarchical-energy-management-strategy

An adaptive hierarchical energy management strategy for hybrid electric vehicles

annotated_deep_learning_paper_implementations

🧑‍🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

attention-learn-to-route

Attention based model for learning to solve different routing problems

awesome-dl-scheduling-papers

awesome-mlco

Awesome machine learning for combinatorial optimization papers.

bevrp

This repository contains code related to solving and visualizing the Bi-Directional Electric Vehicle Routing Problem (B-EVRP) as well as some exemplary data and results.

branch2learn

:trident: Learning to Branch in Mixed Integer Linear Programming with Graph Convolutional Neural Networks in Ecole

carbon_aware_ev_charging

Minimizing Carbon Emission during EV Charging

chargeup

optimizing locations of electric vehicle charging stations in the city of Toronto

dashijia.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

deep-reinforcement-learning-with-pytorch

PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....

deep-spatio-temporal-forecasting-of-electrical-vehicle-charging-demand

densitypeakcluster

Python Code For 'Clustering By Fast Search And Find Of Density Peaks' In Science 2014.

densitypeakcluster-1

Python Code For 'Clustering By Fast Search And Find Of Density Peaks' In Science 2014.（原算法地址：https://github.com/lanbing510/DensityPeakCluster）

dgn

DGN Code

dnn-ed-iot

e2e-model-learning

Task-based end-to-end model learning in stochastic optimization

ecole

Extensible Combinatorial Optimization Learning Environments

electric-vehicle-battery-range-prediction

Predict remaining useful lifetime of an electric car accurately to help drive owner satisfaction and future purchases. This solution comprises analyzing the vast quantity of telemetry data over time and building a Machine Learning model to predict the remaining useful life(RUL) of an electric vehicles EVs at each point in time of the operation of s

electric-vehicle-charge-optimization

Simplified electric vehicle charge optimization based on Hoke (2011).

electric-vehicle-charging

Using electric vehicle charging data, I explore when drivers are likely to plug in their cars, and how much additional electricity demand that will create when the number of electric cars increases.

electric-vehicle-charging-scheduling-model-based-on-lagrange-distributed-algorithm

electric-vehicle-route-planning-on-google-map-reinforcement-learning

User can set up destination for any agent to navigate on Google Map and learn the best route for the agent based on its current condition and the traffic. Our result is 10% less energy consumption than the route provided by Google map

electric_vehicle_charging_scheduler

program that uses reinforced q-learning to come up with optimal electric vehicle charging schedule based on user's driving habits

electric_vehicle_charging_simulation

This project implements Q-Learning to find the optimal policy for charging and discharging electric vehicles in a V2G scheme under conditions of uncertain commitment of EV owners. The problem is modelled as a multi-objective multi-agent cooperative game. Project is part of fulfillment criteria for ECE 730 course at the University of Alberta.

electricvehiclessmartcharging

Smart charging algorithm for Electric vehicles charging stations. This work is supposed to be presented in the 2nd EV integration symposium Stockholm 2018.

elevator-group-control-using-reinforcement-learning-

elevator-scheduling-simulator

:hotel: :office: :department_store: :school: A simulation environment, which mimics the scheduling of multiple elevators within a building of any size. The multithreaded approach helps to simulate elevators running concurrently. The simulation also makes use of several group scheduling algorithms, which help to distribute the workload between elevator-cars as well as make the elevators adaptable to various situations.