greenwilddog

greenwilddog's Stars

• Karl-Zhuo/Dataset-of-HRP-38-test-system

  This repository includes the dataset of HRP-38 system in both .xlsx and .mat file. The index of the selected typical day used in Case Study is also included.

  Language:MATLAB6413

• GenXProject/GenX.jl

  GenX: a configurable power system capacity expansion model for studying low-carbon energy futures. More details at : https://genx.mit.edu

  Language:Julia270115

• Abbhijith-Github/ENV717_S23

  Electric Power Markets - Spring 2023

  Language:Jupyter Notebook16

• SDU-EDC/PowerXLab-Tools

  Simulation and Computing Toolkit for Power System with High Penetration of Renewable Energy Sources

  Language:Python3

• pyaf/load_forecasting

  Forecasting electric power load of Delhi using ARIMA, RNN, LSTM, and GRU models

  Language:Jupyter Notebook495159

• whhsky/COVID-19-KSH

  html+ python +django +爬虫 +pyecharts 实时疫情动态

  Language:JavaScript17357

• Humble-Geralt/GreatGrade

  一个基于Django的机器学习教学辅助，成绩预测平台

  Language:JavaScript134

• LinLidi/StockSensation

  基于情感字典和机器学习的股市舆情情感分类可视化

  Language:Python313115

• PacktPublishing/Machine-Learning-for-the-Web

  Code repository for Machine Learning for the Web, published by Packt

  Language:Jupyter Notebook6347

• ml5js/ml5-library

  Friendly machine learning for the web! 🤖

  Language:JavaScript6.5k899

• oreilly-japan/deep-learning-from-scratch

  『ゼロから作る Deep Learning』(O'Reilly Japan, 2016)

  Language:Jupyter Notebook4k3.4k

• ruanjiaqi01/Residential-Energy-Consumption-Dataset

  6

• FrancescaLazzeri/Machine-Learning-for-Time-Series-Forecasting

  Language:Jupyter Notebook156106

• cador/prediction-python

  《Python预测之美-数据分析与算法实战》代码维护

  Language:Python1810

• HuQi2018/BiSheServer

  本系统是我的毕业设计项目，题目为“基于用户画像的电影推荐系统的设计与实现”。主要是以Django作为基础框架，采用MTV模式，数据库使用MongoDB、MySQL和Redis，以从豆瓣平台爬取的电影数据作为基础数据源，主要基于用户的基本信息和使用操作记录等行为信息来开发用户标签，并使用Hadoop、Spark大数据组件进行分析和处理的推荐系统。管理系统使用的是Django自带的管理系统，并使用simpleui进行了美化。

  Language:Python52751

• otsol/hydrogen-optimization

  Language:Python11

• MLNLP-World/DeepLearning-MuLi-Notes

  Notes about courses Dive into Deep Learning by Mu Li

  Language:Jupyter Notebook3.4k546

• Beepower/sep-mechanism

  Language:MATLAB1

• ames-market/AMES-V5.0

  AMES V5.0 is a version of the AMES Wholesale Power Market Test Bed. It models salient features of U.S RTO/ISO-managed wholesale power markets operating over a high-voltage transmission grid during successive days.

  Language:Java225

• cran/TSclust

  :exclamation: This is a read-only mirror of the CRAN R package repository. TSclust — Time Series Clustering Utilities. Homepage: http://www.jstatsoft.org/v62/i01/

  Language:R95

• mblondel/soft-dtw

  Python implementation of soft-DTW.

  Language:Python54098

• PyPSA/PyPSA

  PyPSA: Python for Power System Analysis

  Language:Python1.2k451

• e2nIEE/pandapower

  Convenient Power System Modelling and Analysis based on PYPOWER and pandas

  Language:Python853480

• yashchandak/SAS_RL

  Language:Python4

• sjtu-marl/malib

  A parallel framework for population-based multi-agent reinforcement learning.

  Language:Python49459

• ShangtongZhang/reinforcement-learning-an-introduction

  Python Implementation of Reinforcement Learning: An Introduction

  Language:Python13.5k4.8k

• TJU-DRL-LAB/AI-Optimizer

  The next generation deep reinforcement learning tookit

  Language:Python4.7k898

• MATPOWER/matpower

  MATPOWER – steady state power flow simulation and optimization for MATLAB and Octave

  Language:MATLAB423151

• joaquimg/BilevelJuMP.jl

  Bilevel optimization in JuMP

  Language:Julia10526

• higgsfield/RL-Adventure

  Pytorch Implementation of DQN / DDQN / Prioritized replay/ noisy networks/ distributional values/ Rainbow/ hierarchical RL

  Language:Jupyter Notebook3k590