Popular GitHub Repositories for Electricity Consumption

TimeGPT-1: production ready pre-trained Time Series Foundation Model for forecasting and anomaly detection. Generative pretrained transformer for time series trained on over 100B data points. It's capable of accurately predicting various domains such as retail, electricity, finance, and IoT with just a few lines of code ?.

A fast Rust based tool to serialize text-based files in a repository or directory for LLM consumption

Tensors, for human consumption

Vector Hub - Library for easy discovery, and consumption of State-of-the-art models to turn data into vectors. (text2vec, image2vec, video2vec, graph2vec, bert, inception, etc)

User can set up destination for any agent to navigate on Google Map and learn the best route for the agent based on its current condition and the traffic. Our result is 10% less energy consumption than the route provided by Google map

? EcoLogits tracks the energy consumption and environmental footprint of using generative AI models through APIs.

A framework to design Reinforcement Learning environments that model Active Network Management (ANM) tasks in electricity distribution networks.

Building Time series forecasting models, including the XGboost Regressor, GRU (Gated Recurrent Unit), LSTM (Long Short-Term Memory), CNN (Convolutional Neural Network), CNN-LSTM, and LSTM-Attention. Additionally, hybrid models like GRU-XGBoost and LSTM-Attention-XGBoost for Electricity Demand and price prediction

A Pytorch DQN and DDPG implementation for a smart home energy management system under varying electricity price.

Prepare files and directories for LLM consumption

Modeling time series of electricity spot prices using Deep Learning.

Forecasting Solar Power: Analysis of using a LSTM Neural Network

Code for the paper "MARL-iDR: Multi-Agent Reinforcement Learning for Incentive-based Residential Demand Response"

simona is an agent-based discrete-event power system simulation model developed @ie3-institute

Optimising electricity expenditure in an HVAC system under dynamic electricity pricing scheme and weather conditions using a DDPG model.

Implementation of RL in the cloud for energy minimization due to migration and excess power consumption.

codebase for "MELTing Point: Mobile Evaluation of Language Transformers"

Desktop application that estimates fuel consumption using a trained machine learning model.

This project is the source code of paper "Optimizing bidding strategy in electricity market based on graph convolutional neural network and deep reinforcement learning" in Applied Energy 2025.

Use local Large Language Models (LLMs) while monitoring energy usage.