60 Repositories
Python energy Libraries
Code for the "Sensing leg movement enhances wearable monitoring of energy expenditure" paper.
EnergyExpenditure Code for the "Sensing leg movement enhances wearable monitoring of energy expenditure" paper. Additional data for replicating this s
(CVPR 2022) Energy-based Latent Aligner for Incremental Learning
Energy-based Latent Aligner for Incremental Learning Accepted to CVPR 2022 We illustrate an Incremental Learning model trained on a continuum of tasks
Home Assistant custom component to help ev-chargers stay below peak hourly energy levels.
Peaqev ev-charging Peaqev ev-charging is an attempt of charging an ev without breaching a preset monthly max-peak energy level. In order for this inte
API for RL algorithm design & testing of BCA (Building Control Agent) HVAC on EnergyPlus building energy simulator by wrapping their EMS Python API
RL - EmsPy (work In Progress...) The EmsPy Python package was made to facilitate Reinforcement Learning (RL) algorithm research for developing and tes
TorchMD-Net provides state-of-the-art graph neural networks and equivariant transformer neural networks potentials for learning molecular potentials
TorchMD-net TorchMD-Net provides state-of-the-art graph neural networks and equivariant transformer neural networks potentials for learning molecular
Artificial Neural network regression model to predict the energy output in a combined cycle power plant.
Energy_Output_Predictor Artificial Neural network regression model to predict the energy output in a combined cycle power plant. Abstract Energy outpu
Easily benchmark PyTorch model FLOPs, latency, throughput, max allocated memory and energy consumption
⏱ pytorch-benchmark Easily benchmark model inference FLOPs, latency, throughput, max allocated memory and energy consumption Install pip install pytor
IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling
IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling This is my code, data and approach for the IEEE-CIS Technical Challen
An energy estimator for eyeriss-like DNN hardware accelerator
Energy-Estimator-for-Eyeriss-like-Architecture- An energy estimator for eyeriss-like DNN hardware accelerator This is an energy estimator for eyeriss-
Python based utilities for interacting with digital multimeters that are built on the FS9721-LP3 chipset.
Python based utilities for interacting with digital multimeters that are built on the FS9721-LP3 chipset.
Born-Infeld (BI) for AI: Energy-Conserving Descent (ECD) for Optimization
Born-Infeld (BI) for AI: Energy-Conserving Descent (ECD) for Optimization This repository contains the code for the BBI optimizer, introduced in the p
Openfe - Alchemical free energy calculations for the masses
The Open Free Energy library Alchemical free energy calculations for the masses.
On Out-of-distribution Detection with Energy-based Models
On Out-of-distribution Detection with Energy-based Models This repository contains the code for the experiments conducted in the paper On Out-of-distr
Equipped customers with insights about their EVs Hourly energy consumption and helped predict future charging behavior using LSTM model
Equipped customers with insights about their EVs Hourly energy consumption and helped predict future charging behavior using LSTM model. Designed sample dashboard with insights and recommendation for customers.
The Official PyTorch Implementation of "VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models" (ICLR 2021 spotlight paper)
Official PyTorch implementation of "VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models" (ICLR 2021 Spotlight Paper) Zhisheng
CO2Ampel - This RaspberryPi project uses weather data to estimate the share of renewable energy in the power grid
CO2Ampel This RaspberryPi project uses weather data to estimate the share of ren
Energy consumption estimation utilities for Jetson-based platforms
This repository contains a utility for measuring energy consumption when running various programs in NVIDIA Jetson-based platforms. Currently TX-2, NX, and AGX are supported.
Madanalysis5 - A package for event file analysis and recasting of LHC results
Welcome to MadAnalysis 5 Outline What is MadAnalysis 5? Requirements Downloading
GPOEO is a micro-intrusive GPU online energy optimization framework for iterative applications
GPOEO GPOEO is a micro-intrusive GPU online energy optimization framework for iterative applications. We also implement ODPP [1] as a comparison. [1]
Flask app to predict daily radiation from the time series of Solcast from Islamabad, Pakistan
Solar-radiation-ISB-MLOps - Flask app to predict daily radiation from the time series of Solcast from Islamabad, Pakistan.
Reduce end to end training time from days to hours (or hours to minutes), and energy requirements/costs by an order of magnitude using coresets and data selection.
COResets and Data Subset selection Reduce end to end training time from days to hours (or hours to minutes), and energy requirements/costs by an order
This code extracts line width of phonons from specular energy density (SED) calculated with LAMMPS.
This code extracts line width of phonons from specular energy density (SED) calculated with LAMMPS.
Python client and API for monitoring and controling energy diversion devices from MyEnergi
Python client and API for monitoring and controling energy diversion devices from MyEnergi A set of library functions and objects for interfacing with
Official code for 'Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentationon Complex Urban Driving Scenes'
PEBAL This repo contains the Pytorch implementation of our paper: Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urb
Python Yeelight YLKG07YL/YLKG08YL dimmer handler
With this class you can receive, decrypt and handle Yeelight YLKG07YL/YLKG08YL dimmer bluetooth notifications in your python code.
Official code for 'Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentationon Complex Urban Driving Scenes'
PEBAL This repo contains the Pytorch implementation of our paper: Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentationon Complex Urba
[NeurIPS 2021 Spotlight] Code for Learning to Compose Visual Relations
Learning to Compose Visual Relations This is the pytorch codebase for the NeurIPS 2021 Spotlight paper Learning to Compose Visual Relations. Demo Imag
Experimental code for paper: Generative Adversarial Networks as Variational Training of Energy Based Models
Experimental code for paper: Generative Adversarial Networks as Variational Training of Energy Based Models, under review at ICLR 2017 requirements: T
Generative Adversarial Networks for High Energy Physics extended to a multi-layer calorimeter simulation
CaloGAN Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks. This repository c
Training, generation, and analysis code for Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics
Location-Aware Generative Adversarial Networks (LAGAN) for Physics Synthesis This repository contains all the code used in L. de Oliveira (@lukedeo),
Using graph_nets for pion classification and energy regression. Contributions from LLNL and LBNL
nbdev template Use this template to more easily create your nbdev project. If you are using an older version of this template, and want to upgrade to
Multi-objective constrained optimization for energy applications via tree ensembles
Multi-objective constrained optimization for energy applications via tree ensembles
CityLearn Challenge Multi-Agent Reinforcement Learning for Intelligent Energy Management, 2020, PikaPika team
Citylearn Challenge This is the PyTorch implementation for PikaPika team, CityLearn Challenge Multi-Agent Reinforcement Learning for Intelligent Energ
This package is a python library with tools for the Molecular Simulation - Software Gromos.
This package is a python library with tools for the Molecular Simulation - Software Gromos. It allows you to easily set up, manage and analyze simulations in python.
Perturb-and-max-product: Sampling and learning in discrete energy-based models
Perturb-and-max-product: Sampling and learning in discrete energy-based models This repo contains code for reproducing the results in the paper Pertur
[NeurIPS 2021] Code for Unsupervised Learning of Compositional Energy Concepts
Unsupervised Learning of Compositional Energy Concepts This is the pytorch code for the paper Unsupervised Learning of Compositional Energy Concepts.
This is the accompanying repository for the Bloomberg Global Coal Countdown website.
This is the accompanying repository for the Bloomberg Global Coal Countdown (BGCC) website. Data Sources Dashboard Data Schema and Validation License
How will electric vehicles affect traffic congestion and energy consumption: an integrated modelling approach
EV-charging-impact This repository contains the code that has been used for the Queue modelling for the paper "How will electric vehicles affect traff
Absolute solvation free energy calculations with OpenFF and OpenMM
ABsolute SOLVantion Free Energy Calculations The absolv framework aims to offer a simple API for computing the change in free energy when transferring
Some toy examples of score matching algorithms written in PyTorch
toy_gradlogp This repo implements some toy examples of the following score matching algorithms in PyTorch: ssm-vr: sliced score matching with variance
Official implementation of "Learning Proposals for Practical Energy-Based Regression", 2021.
ebms_proposals Official implementation (PyTorch) of the paper: Learning Proposals for Practical Energy-Based Regression, 2021 [arXiv] [project]. Fredr
An adaptive hierarchical energy management strategy for hybrid electric vehicles
An adaptive hierarchical energy management strategy This project contains the source code of an adaptive hierarchical EMS combining heuristic equivale
PSML: A Multi-scale Time-series Dataset for Machine Learning in Decarbonized Energy Grids
PSML: A Multi-scale Time-series Dataset for Machine Learning in Decarbonized Energy Grids The electric grid is a key enabling infrastructure for the a
PyPSA: Python for Power System Analysis
1 Python for Power System Analysis Contents 1 Python for Power System Analysis 1.1 About 1.2 Documentation 1.3 Functionality 1.4 Example scripts as Ju
Control System Packer is a lightweight, low-level program to transform energy equations into the compact libraries for control systems.
Control System Packer is a lightweight, low-level program to transform energy equations into the compact libraries for control systems. Packer supports Python 🐍 , C 💻 and C++ 💻 libraries.
PyTorch code accompanying our paper on Maximum Entropy Generators for Energy-Based Models
Maximum Entropy Generators for Energy-Based Models All experiments have tensorboard visualizations for samples / density / train curves etc. To run th
Code for paper "Energy-Constrained Compression for Deep Neural Networks via Weighted Sparse Projection and Layer Input Masking"
model_based_energy_constrained_compression Code for paper "Energy-Constrained Compression for Deep Neural Networks via Weighted Sparse Projection and
Custom component for interacting with Octopus Energy
Home Assistant Octopus Energy ** WARNING: This component is currently a work in progress ** Custom component built from the ground up to bring your Oc
Produces a summary CSV report of an Amber Electric customer's energy consumption and cost data.
Amber Electric Usage Summary This is a command line tool that produces a summary CSV report of an Amber Electric customer's energy consumption and cos
emhass: Energy Management for Home Assistant
emhass EMHASS: Energy Management for Home Assistant Context This module was conceived as an energy management optimization tool for residential electr
Calculates carbon footprint based on fuel mix and discharge profile at the utility selected. Can create graphs and tabular output for fuel mix based on input file of series of power drawn over a period of time.
carbon-footprint-calculator Conda distribution ~/anaconda3/bin/conda install anaconda-client conda-build ~/anaconda3/bin/conda config --set anaconda_u
Home Assistant integration for energy consumption data from UK SMETS (Smart) meters using the Hildebrand Glow API.
Hildebrand Glow (DCC) Integration Home Assistant integration for energy consumption data from UK SMETS (Smart) meters using the Hildebrand Glow API. T
A web app for presenting my research in BEM(building energy model) simulation
BEM(building energy model)-SIM-APP The is a web app presenting my research in BEM(building energy model) calibration. You can play around with some pa
Uses the Duke Energy Gateway to import near real time energy usage into Home Assistant
Duke Energy Gateway This is a custom integration for Home Assistant. It pulls near-real-time energy usage from Duke Energy via the Duke Energy Gateway
the code for paper "Energy-Based Open-World Uncertainty Modeling for Confidence Calibration"
EOW-Softmax This code is for the paper "Energy-Based Open-World Uncertainty Modeling for Confidence Calibration". Accepted by ICCV21. Usage Commnd exa
Orthogonal Over-Parameterized Training
The inductive bias of a neural network is largely determined by the architecture and the training algorithm. To achieve good generalization, how to effectively train a neural network is of great importance. We propose a novel orthogonal over-parameterized training (OPT) framework that can provably minimize the hyperspherical energy which characterizes the diversity of neurons on a hypersphere. See our previous work -- MHE for an in-depth introduction.
Learning Energy-Based Models by Diffusion Recovery Likelihood
Learning Energy-Based Models by Diffusion Recovery Likelihood Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, Diederik P. Kingma Paper: https://arxiv.o
PyTorch implementation of Algorithm 1 of "On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models"
Code for On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models This repository will reproduce the main results from our pape
The windML framework provides an easy-to-use access to wind data sources within the Python world, building upon numpy, scipy, sklearn, and matplotlib. Renewable Wind Energy, Forecasting, Prediction
windml Build status : The importance of wind in smart grids with a large number of renewable energy resources is increasing. With the growing infrastr
💡 Learnergy is a Python library for energy-based machine learning models.
Learnergy: Energy-based Machine Learners Welcome to Learnergy. Did you ever reach a bottleneck in your computational experiments? Are you tired of imp