cxy2333355's Stars
jackfrued/Python-100-Days
Python - 100天从新手到大师
fchollet/deep-learning-with-python-notebooks
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
resumejob/awesome-resume
Resume，Resume Templates，程序员简历例句，简历模版，
wzdnzd/aggregator
One-stop Proxies Crawling and Aggregation Platform
ai-dawang/PlugNPlay-Modules
XifengGuo/CapsNet-Keras
A Keras implementation of CapsNet in NIPS2017 paper "Dynamic Routing Between Capsules". Now test error ＝ 0.34%.
acados/acados
Fast and embedded solvers for nonlinear optimal control
HiroIshida/robust-tube-mpc
Example implementation for robust model predictive control using tube
deng-haoyang/ParNMPC
A Parallel Optimization Toolkit for Nonlinear Model Predictive Control (NMPC)
pypsa-meets-earth/pypsa-earth
PyPSA-Earth: A flexible Python-based open optimisation model to study energy system futures around the world.
Secilia-Cxy/SOFTS
Official implement for "SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion"(NeurIPS'24) in PyTorch.
tahanakabi/DRL-for-microgrid-energy-management
We study the performance of various deep reinforcement learning algorithms for the problem of microgrid’s energy management system. We propose a novel microgrid model that consists of a wind turbine generator, an energy storage system, a population of thermostatically controlled loads, a population of price-responsive loads, and a connection to the main grid. The proposed energy management system is designed to coordinate between the different sources of flexibility by defining the priority resources, the direct demand control signals and the electricity prices. Seven deep reinforcement learning algorithms are implemented and empirically compared in this paper. The numerical results show a significant difference between the different deep reinforcement learning algorithms in their ability to converge to optimal policies. By adding an experience replay and a second semi-deterministic training phase to the well-known Asynchronous advantage actor critic algorithm, we achieved considerably better performance and converged to superior policies in terms of energy efficiency and economic value.
robinhenry/gym-anm
A framework to design Reinforcement Learning environments that model Active Network Management (ANM) tasks in electricity distribution networks.
GMLC-TDC/HELICS
Hierarchical Engine for Large-scale Infrastructure Co-Simulation (HELICS)
chennnnnyize/LLM_PowerSystems
openego/eDisGo
Optimization of flexibility options and grid expansion for distribution grids based on PyPSA
harrynapier/Power-system-optimization
This is the project containing the code developed for mulity objective optimization of microgrid energy management systems with user cooperation
AylaRT/ACTER
ACTER is a manually annotated dataset for term extraction, covering 3 languages (English, French, and Dutch), and 4 domains (corruption, dressage, heart failure, and wind energy).
Yorkson-huang/CNN-LSTM-Attention-Prediction
masoudshab/OPTIMIZATION-of-the-APFs-Placement-Based-on-Instantaneous-Reactive-Power-Theory-by-GENETIC-ALGORITHM
In electrical distribution systems, a great amount of power are wasting across the lines, also nowadays power factors, voltage profiles and total harmonic distortions (THDs) of most loads are not as would be desired. So these important parameters of a system play highly important role in wasting money and energy, and besides both consumers and sources are suffering from a high rate of distortions and even instabilities. Active power filters (APFs) are innovative ideas for solving of this adversity which have recently used instantaneous reactive power theory. In this paper, a novel method is proposed to optimize the allocation of APFs. The introduced method is based on the instantaneous reactive power theory in vectorial representation. By use of this representation, it is possible to asses different compensation strategies. Also, APFs proper placement in the system plays a crucial role in either reducing the losses costs and power quality improvement. To optimize the APFs placement, a new objective function has been defined on the basis of five terms: total losses, power factor, voltage profile, THD and cost. Genetic algorithm has been used to solve the optimization problem. The results of applying this method to a distribution network illustrate the method advantages.
JonasSievers/Transformer-based-Federated-Learning-for-Load-Forecasting
Source code for our ICCEP paper "Secure short-term load forecasting for smart grids with transformer-based federated learning".
SALAWUDEEN/OPTIMAL-SIZING-OF-HYBRID-RENEWABLE-ENERGY-SYSTEM-USING-NEW-VARIANTS-OF-SMELL-AGENT-OPTIMIZATION
In this repository, Three variants of Smell Agent Optimization (SAO) is used to solve the optimal sizing of Hybrid Renewable Energy System (HRES). The HRES is modeled to include Wind Turbine, PV and Storage System. Feel free to use the code or implement the idea used in the variants of SAO for other algorithms. Check Read Me for Instructions.
frostyduck/Power-Flexibility-Project
Some program codes for the RSF-DFG project "Development of Innovative Technologies and Tools for Flexibility Assessment and Enhancement of Future Power Systems"
JonasSievers/Mixture-of-Experts-based-Federated-Learning-for-Energy-Forecasting
Source code for our preprint paper "Advancing Accuracy in Load Forecasting using Mixture-ofExperts and Federated Learning".
JessiYang0/Multi-Task-Learning-Model
this work proposes a deep learning model based on multi-task learning architecture to predict the hourly electricity load. In our proposed architecture, the main task is to predict the electricity load, while the auxiliary task is to predict the temperature.
BinHuangScut/QTCN
This repository holds the code and data for "Multi-task Learning Based Attentive Quantile Regression Temporal Convolutional Network for Multi-energy Probabilistic Load Forecasting"
DelbertWang2/TemporalGraphSR
This is the code of the algorithm proposed in “Temporal Graph Super Resolution on Power Distribution Network Measurements”. If this code is useful to you, we will be glad to see our paper being cited, thanks!
sushoujiuqin/reinforcement-learning-in-microgrid
In this repository, I will show the application of RL in microgrid
mmiller96/RESOptimizationWeakGrid
Algorithms for optimal renewable and hydrogen integration in weak grids, boosting efficiency and stability. Based on the paper 'Optimal allocation of Renewable Energy Systems in a Weak Distribution Network.'
qqyi111/UCI-Power-Plant
The dataset contains 9568 data points collected from a Combined Cycle Power Plant over 6 years (2006-2011), when the plant was set to work with full load.