smart-grid

There are 109 repositories under smart-grid topic.

  • zhgqcn/awesome-NILM-with-code

    A repository of awesome Non-Intrusive Load Monitoring(NILM) with code.

    1163023
  • dlms-cosem

    u9n/dlms-cosem

    A Python library for DLMS/COSEM

    Language:Python10545652

  • pwitab/iec62056-21

    A Python library for IEC62056-21, Local Data Readout of Energy Meters. Former IEC1107

    Language:Python7982424

  • mesmo-dev/mesmo

    MESMO - Multi-Energy System Modeling and Optimization

    Language:Python56112314
  • ESA

    mzy2240/ESA

    Easy SimAuto (ESA): An easy-to-use Power System Analysis Automation Environment atop PowerWorld Simulator Automation Server (SimAuto)

    Language:Python4749913

  • skarapost/EVLib

    EVLib is a library for the management and the simulation of Electric Vehicle (EV) activities, at a charging station level, within a Smart Grid environment.

    Language:Java46449

  • xuwkk/DDET-MTD

    This repo contains all the codes and data for 'Blending Data and Physics Against False Data Injection Attack: An Event-Triggered Moving Target Defence Approach'

    Language:Jupyter Notebook36125

  • abyss-soft/webpack-template-base

    Готовая сборка webpack c сеткой smart-grid

    Language:SCSS273427

  • AcerWang/scdDiagram

    smart substation connection and configuration software based on IEC 61850 protocal and SCD file. Email: 570503271@qq.com

    Language:Python25415

  • crond-jaist/GridAttackSim

    GridAttackSim: Smart Grid Attack Simulation Framework

    Language:Zimpl231510

  • fnesveda/DemandManagement

    A diploma thesis investigating the options of controlling power demand of households to reduce peaks in total power consumption in smart grids.

    Language:Python19203

  • MateoGreil/homeassistant-comwatt

    Comwatt Integration for HomeAssistant

    Language:Python184274

  • jddeguia/compare-forecast-models

    Energy production of photovoltaic (PV) system is heavily influenced by solar irradiance. Accurate prediction of solar irradiance leads to optimal dispatching of available energy resources and anticipating end-user demand. However, it is difficult to do due to fluctuating nature of weather patterns.  In the study, neural network models were defined to predict solar irradiance values based on weather patterns. Models included in the study are artificial neural network, convolutional neural network, bidirectional long-short term memory (LSTM) and stacked LSTM.  Preprocessing methods such as data normalization and principal component analysis were applied before model training. Regression metrics such as mean squared error (MSE), maximum residual error (max error), mean absolute error (MAE), explained variance score (EVS), and regression score function (R2 score), were used to evaluate the performance of model prediction. Plots such as prediction curves, learning curves, and histogram of error distribution were also considered as well for further analysis of model performance. All models showed that it is capable of learning unforeseen values, however, stacked LSTM has the best results with the max error, R2, MAE, MSE, and EVS values of 651.536, 0.953, 41.738, 5124.686, and 0.946, respectively.

    Language:Jupyter Notebook17104

  • bitstoenergy/iclr-tutorial

    Smart Meter Data Analytics Tutorial @ 11th International Conference on Learning Representations (ICLR 2023)

    Language:Jupyter Notebook16006

  • ElsevierSoftwareX/SOFTX-D-20-00087

    pycity_scheduling - A Python framework for the development and assessment of optimization-based power scheduling algorithms for multi-energy systems in city districts. To cite this Original Software Publication: https://www.sciencedirect.com/science/article/pii/S2352711021001230

    Language:Python15225

  • nicomignoni/Multiple-storage-systems-in-smart-grids

    Simulations code for MSc thesis.

    Language:MATLAB14213

  • supsi-dacd-isaac/krangpower

    Distribution System Simulator based on OpenDSS and OpenDSSDirect.py. Modern Syntax, DataFrames, Pint, Networkx, Algorithmic Agents.

    Language:Python14911

  • Ildar-Daminov/Assessment_Dynamic_Thermal_Rating_of_Transformers

    MATLAB code and data for the article 📋: I. Daminov, A. Prokhorov, R. Caire, M-C Alvarez-Herault, “Assessment of dynamic transformer rating, considering current and temperature limitations” in International Journal of Electrical Power & Energy Systems (IF: 3,588, Q1), 2021

    Language:MATLAB12101

  • jddeguia/bagging-lstm

    Implementation of bagging-based ensemble for solar irradiance prediction. Base learners used in ensemble learning is stacked-LSTM

    Language:Jupyter Notebook12112

  • nicomignoni/Smart-Grid-Centralized-Opt

    Scripts for a university project, a simple centralized smart grid energy cost minimization problem.

    Language:MATLAB12111

  • SyedHasnat/Papers

    Contains the code for the paper "Multi-Horizon Short-Term Load Forecasting Using Hybrid of LSTM and Modified Split Convolution"

    Language:Jupyter Notebook10134

  • denisraymer/richbee-relation

    HTML layout for Richbee. The parallax effect has been implemented. System of modal windows with warnings. The development used GULP, BEM, BABEL, WEBPACK, SCSS, SMARTGRID

    Language:HTML9100

  • sustainable-computing/deadline-aware-fair-scheduling

    A Deadline-Aware, Incentive-Compatible and Proportionally-Fair Mechanism for EV Charging in Distribution Grids

    Language:Python9101

  • kchristidis/island

    A simulator for blockchain-based local energy markets

    Language:Go8401

  • NeeleKemper/moevorl-ev-cm

    A Comparative Study of Multi-Objective and Neuroevolutionary-based Reinforcement Learning Algorithms for Optimizing Electric Vehicle Charging and Load Management

    Language:HTML8100

  • Pratik-Harsh/Matlab_Code

    Two stage heuristic method to reconfigure the distribution networks

    Language:MATLAB8100

  • antonoliv/grl-ded

    A Graph Reinforcement Learning model that combines RL and Graph Neural Networks to solve Dynamic Economic Power Dispatch

    Language:TeX7200
  • sparc

    smoke-trees/sparc

    Smart Grid solution which compiles home networks and grids in an effecient manner, controlled by recurrent networks which predict distribution and consumption and also supported by an energy credit system. All running as microservices supporting each other.

    Language:Jupyter Notebook7324

  • FlorianDe/power-grid-gans

    This project contains an extensible GAN Framework which can be used to generate power grid related data for simulations.

    Language:Python6205

  • MateoGreil/python-comwatt-client

    A Python client library for interacting with the Comwatt API.

    Language:Python6122

  • SaM-92/energy-data-entsoe

    The ENTSO-E Data Analysis Tool is an interactive web application designed to streamline the analysis of the European Network of Transmission System Operators for Electricity (ENTSO-E) power system data. This tool is crafted to facilitate a seamless operation in handling, visualising, and analysing electricity market and grid data across Europe.

    Language:Python6100

  • denisraymer/top-news-israel

    News site layout using GULP, BEM, BABEL, WEBPACK, SCSS, SMART-GRID

    Language:HTML4160

  • nishatrhythm/Power-Distributors-of-Bangladesh

    This is an API of the power distribution companies in Bangladesh. It includes details of the BPDB, DESCO, DPDC, NESCO, WZPDCL, BREB.

    Language:Python4100

  • pwitab/iflag

    A Python library for the Itron / Actaris IFLAG and Corus protocol

    Language:Python4153

  • Dellintel98/smart-nanogrid-gym

    Smart Nanogrid Gym is an OpenAI Gym environment for simulation of a smart nanogrid incorporating renewable energy systems, battery energy storage systems, electric vehicle charging station, grid connection, a connected building and using vehicle-to-everything methodology.

    Language:Jupyter Notebook3102

  • rthandi/smartGridSimulation

    An implementation of a peer to peer smart grid trading system. Utilising homomorphic encryption to allow for a semi trusted third party to carry out operations on the trading data without knowledge of private data. See more at: https://ieeexplore.ieee.org/document/9817551

    Language:Python3100