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ai-to-forecast-constraints-in-the-energy-systems

This thesis proposes the enhancing of state estimation and constraint detection of the transmission and distribution grids through the use of machine learning methods. This contribution is relevant once the current methodology suffers from inaccurate or lack of system information in order to perform numerical methods. Taking into consideration the several machine learning techniques presented in the literature, a range of models will be tested in the framing of both classification and regression problems, with the aim of achieving accurate predictions.

arga

This is a TensorFlow implementation of the Adversarially Regularized Graph Autoencoder(ARGA) model as described in our paper: Pan, S., Hu, R., Long, G., Jiang, J., Yao, L., & Zhang, C. (2018). Adversarially Regularized Graph Autoencoder for Graph Embedding, [https://www.ijcai.org/proceedings/2018/0362.pdf].

benchmarking_pgnn

This repositories contains the source code to carry out benchmarking of position aware graph neural networks

building-data-genome-project-2

Whole building non-residential hourly energy meter data from the Great Energy Predictor III competition

captainblackboard

船长关于机器学习、计算机视觉和工程技术的总结和分享

chatgpt_academic

科研工作专用ChatGPT拓展，特别优化学术Paper润色体验，支持自定义快捷按钮，支持markdown表格显示，Tex公式双显示，代码显示功能完善，新增本地Python工程剖析功能/自我剖析功能

chatpaper

Use ChatGPT to summary the Arxiv papers.

circleloss

Pytorch implementation of the paper "Circle Loss: A Unified Perspective of Pair Similarity Optimization"

clustergcn

A PyTorch implementation of "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks" (KDD 2019).

compgcn

ICLR 2020: Composition-Based Multi-Relational Graph Convolutional Networks

cs224w_winter2021

CS224W Stanford Winter 2021 Homework solutions

ctr-gcn

[ICCV2021] Official code for "Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition"

cvx_short_course

Materials for a short course on convex optimization.

cvxpy

A Python-embedded modeling language for convex optimization problems.

deep-generative-network

deep-q-networks

Implementations of algorithms from the Q-learning family. Implementations inlcude: DQN, DDQN, Dueling DQN, PER+DQN, Noisy DQN, C51

deep_gcn_benchmarking

[Preprint] "Bag of Tricks for Training Deeper Graph Neural Networks A Comprehensive Benchmark Study" by Tianlong Chen*, Kaixiong Zhou*, Keyu Duan, Wenqing Zheng, Peihao Wang, Xia Hu, Zhangyang Wang

deep_reinforcement_learning_course

Implementations from the free course Deep Reinforcement Learning with Tensorflow and PyTorch

demo-net

DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification

dgcnn

A PyTorch implementation of DGCNN based on AAAI 2018 paper "An End-to-End Deep Learning Architecture for Graph Classification"

dgcnn-1

dist_grid_identification

A simulation framework for topology identification and model parameter estimation in power distribution grids: https://ieeexplore.ieee.org/document/8601410

distribution-grid-data-source

A collection of distribution power grid data source

distribution-system-model-calibration

Algorithms for calibrating power grid distribution system models

dizhi

雨花阁发布页

dqn_pytorch

Vanilla DQN, Double DQN, and Dueling DQN implemented in PyTorch

dyhatr

DyHATR (ECML-PKDD 2020)

dyrep_torch

evolvegcn

Code for EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs

featuretools

An open source python library for automated feature engineering