Time series observation and valid action handling for applying deep reinforcement learning in microgrid energy management.

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

Clean and robust implementations of Reinforcement Learning algorithms by Pytorch

PyPSA: Python for Power System Analysis

Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学

12 Weeks, 24 Lessons, AI for All!

Companion notebooks for the B&E paper on 'Ten questions concerning data-driven modelling and forecasting of operational energy demand at building and urban scale'

Concise pytorch implements of DRL algorithms, including REINFORCE, A2C, DQN, PPO(discrete and continuous), DDPG, TD3, SAC.

A Pytorch DQN and DDPG implementation for a smart home energy management system under varying electricity price.

Hybrid Gaussian Process CC-OPF with sparse approximation

A tutorial on forecasting electricity demand.

Books To Master Deep Learning

To keep track and showcase

Config files for my GitHub profile.

A Deep Reinforcement Learning based approach for energy supply management in MicroGrids

An optimization project on the unit commitment problem and its MILP solution implemented

This example walks through the process of developing an optimization routine that uses forecast pricing and loading conditions to optimally store/sell energy from a grid-scale battery system.

Using Pytorch to implement TD3 and play with bipedal