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-scenemover

Project of Siggraph Asia 2020 paper: Scene Mover: Automatic Move Planning for Scene Arrangement by Deep Reinforcement Learning

drlforpowersystemrecovery

Master Thesis Project for DRL in power system restoration using renewables

dyreson_etal_2021_earthsfuture

Regional Connections are Key to Planning for Future Power System Operations under Climate Extremes

forecast-electricity-load-demand-using-deep-learning-models

Forecast the Short Term Electricity Load Demand for Panama Power System using Deep Learning Models

grid-ml

Power grid optimization problem solvers

grid2op

Grid2Op a testbed platform to model sequential decision making in power systems.

intellihealer

IntelliHealer: An imitation and reinforcement learning platform for self-healing distribution networks

load_forecasting_web_system

The project aims to use machine learning technology to achieve high-precision day-ahead load forecasting, to provide a reliable basis for market scheduling and market pricing, and to provide support for the formulation of urban power equipment maintenance plans.

mes_resilience

model-to-predict-energy-consumption-city-of-seattle

Use Seattle's public energy data and build a model predicting energy consumption

multi-feature-power-load-forecasting-based-on-deep-learning

基于深度学习的多特征电力负荷预测

passenger

powergridworld

PowerGridworld provides users with a lightweight, modular, and customizable framework for creating power-systems-focused, multi-agent Gym environments that readily integrate with existing training frameworks for reinforcement learning (RL). https://arxiv.org/abs/2111.05969

powersystems.jl

Data structures in Julia to enable power systems analysis. Part of the Scalable Integrated Infrastructure Planning Initiative at the National Renewable Energy Lab.

reinforcement-learning_for_energy_minimization_using_cloudsim

Implementation of RL in the cloud for energy minimization due to migration and excess power consumption.

robustacopf

Codes and data supplemental files for the paper "Robust Optimization for Electricity Generation"

short-term-wind-speed-prediction-based-on-deep-learning

LSTM neural network realizes the prediction of wind speed through the learning of various parameters. It can provide important support for the smooth operation of power system and the optimization of control strategy. The fuzzy rough set theory is used to reduce many factors that affect wind speed. It simplifies the input of the neural network prediction model and improves the accuracy and speed. Compared with the traditional neural network prediction method, MAE and MAPE in FRS-LSTM wind speed forecasting model have decreased and the accuracy has been improved greatly.

smart-grid-vpp-creation-simulation

Optimization of Virtual Power Plant formation process using Game Theory

soe

This is the Matlab code for the Switch Opening and Exchange (SOE) method used in paper "Switch Opening and Exchange Method for Stochastic Distribution Network Reconfiguration" that has been accepted by and will be published in IEEE Transactions on Smart Grid.

transactive-energy-with-reinforcment-learning-deep-q-network

Energy trading using DQN