Best AI tools for< Electricity Market Researcher >

Infographic

3 - AI tool Sites

Personalized.energy

Personalized.energy is an AI-powered online platform that offers personalized electricity plans tailored to individual needs and lifestyles. The platform simplifies the process of finding the best energy solution by utilizing an AI-powered search engine to compare and match plans based on users' location and usage profiles. By eliminating the need for manual research and comparison, Personalized.energy provides a stress-free experience for users looking to navigate the complexities of the energy market.

site

: 2.6k

Wattle

Wattle is a modern business platform designed for home service professionals. It offers intelligent software that simplifies and automates operations, allowing professionals to focus more on customer interactions. With features like AI-powered booking and dispatching, online bookings, automated quotations, and seamless integration with existing software, Wattle streamlines job lifecycles and enhances productivity. The platform caters to various service industries such as HVAC, plumbing, lawn care, electrical, and pest control, providing a comprehensive solution for managing quotes, teams, payments, and scheduling. Wattle aims to revolutionize service businesses by leveraging AI technology to optimize processes and improve customer engagement.

site

: 0

Building Services AI

Building Services AI is an AI-driven platform that simplifies technical insights for building services professionals. It harnesses advanced AI technology to provide expert-level guidance on various aspects of building services, including HVAC systems, electrical setups, plumbing, and more. The platform offers comprehensive knowledge, maintenance insights, and 24/7 assistance to help users understand how building systems work and how to maintain them efficiently. Building Services AI aims to be a trusted resource for building operations and maintenance, offering expert advice and expertise to navigate the complexities of the field with confidence.

site

: 0

14 - Open Source Tools

awesome-green-ai

Awesome Green AI is a curated list of resources and tools aimed at reducing the environmental impacts of using and deploying AI. It addresses the carbon footprint of the ICT sector, emphasizing the importance of AI in reducing environmental impacts beyond GHG emissions and electricity consumption. The tools listed cover code-based tools for measuring environmental impacts, monitoring tools for power consumption, optimization tools for energy efficiency, and calculation tools for estimating environmental impacts of algorithms and models. The repository also includes leaderboards, papers, survey papers, and reports related to green AI and environmental sustainability in the AI sector.

github

: 68

ML-news-of-the-week

github

: 129

ChatGPT

github

: 67

awesome-ChatGPT-repositories

github

: 2.4k

nixtla

Nixtla is a production-ready generative pretrained transformer for time series forecasting and anomaly detection. It can accurately predict various domains such as retail, electricity, finance, and IoT with just a few lines of code. TimeGPT introduces a paradigm shift with its standout performance, efficiency, and simplicity, making it accessible even to users with minimal coding experience. The model is based on self-attention and is independently trained on a vast time series dataset to minimize forecasting error. It offers features like zero-shot inference, fine-tuning, API access, adding exogenous variables, multiple series forecasting, custom loss function, cross-validation, prediction intervals, and handling irregular timestamps.

github

: 2.6k

AIforEarthDataSets

The Microsoft AI for Earth program hosts geospatial data on Azure that is important to environmental sustainability and Earth science. This repo hosts documentation and demonstration notebooks for all the data that is managed by AI for Earth. It also serves as a "staging ground" for the Planetary Computer Data Catalog.

github

: 263

ai-dev-2024-ml-workshop

The 'ai-dev-2024-ml-workshop' repository contains materials for the Deploy and Monitor ML Pipelines workshop at the AI_dev 2024 conference in Paris, focusing on deployment designs of machine learning pipelines using open-source applications and free-tier tools. It demonstrates automating data refresh and forecasting using GitHub Actions and Docker, monitoring with MLflow and YData Profiling, and setting up a monitoring dashboard with Quarto doc on GitHub Pages.

github

: 93

AI-on-the-edge-device

AI-on-the-edge-device is a project that enables users to digitize analog water, gas, power, and other meters using an ESP32 board with a supported camera. It integrates Tensorflow Lite for AI processing, offers a small and affordable device with integrated camera and illumination, provides a web interface for administration and control, supports Homeassistant, Influx DB, MQTT, and REST API. The device captures meter images, extracts Regions of Interest (ROIs), runs them through AI for digitization, and allows users to send data to MQTT, InfluxDb, or access it via REST API. The project also includes 3D-printable housing options and tools for logfile management.

github

: 6.9k

CALF

CALF (LLaTA) is a cross-modal fine-tuning framework that bridges the distribution discrepancy between temporal data and the textual nature of LLMs. It introduces three cross-modal fine-tuning techniques: Cross-Modal Match Module, Feature Regularization Loss, and Output Consistency Loss. The framework aligns time series and textual inputs, ensures effective weight updates, and maintains consistent semantic context for time series data. CALF provides scripts for long-term and short-term forecasting, requires Python 3.9, and utilizes word token embeddings for model training.

github

: 60

LLMEvaluation

The LLMEvaluation repository is a comprehensive compendium of evaluation methods for Large Language Models (LLMs) and LLM-based systems. It aims to assist academics and industry professionals in creating effective evaluation suites tailored to their specific needs by reviewing industry practices for assessing LLMs and their applications. The repository covers a wide range of evaluation techniques, benchmarks, and studies related to LLMs, including areas such as embeddings, question answering, multi-turn dialogues, reasoning, multi-lingual tasks, ethical AI, biases, safe AI, code generation, summarization, software performance, agent LLM architectures, long text generation, graph understanding, and various unclassified tasks. It also includes evaluations for LLM systems in conversational systems, copilots, search and recommendation engines, task utility, and verticals like healthcare, law, science, financial, and others. The repository provides a wealth of resources for evaluating and understanding the capabilities of LLMs in different domains.

github

: 94

Awesome-TimeSeries-SpatioTemporal-LM-LLM

Awesome-TimeSeries-SpatioTemporal-LM-LLM is a curated list of Large (Language) Models and Foundation Models for Temporal Data, including Time Series, Spatio-temporal, and Event Data. The repository aims to summarize recent advances in Large Models and Foundation Models for Time Series and Spatio-Temporal Data with resources such as papers, code, and data. It covers various applications like General Time Series Analysis, Transportation, Finance, Healthcare, Event Analysis, Climate, Video Data, and more. The repository also includes related resources, surveys, and papers on Large Language Models, Foundation Models, and their applications in AIOps.

github

: 944

Rankify

Rankify is a Python toolkit designed for unified retrieval, re-ranking, and retrieval-augmented generation (RAG) research. It integrates 40 pre-retrieved benchmark datasets and supports 7 retrieval techniques, 24 state-of-the-art re-ranking models, and multiple RAG methods. Rankify provides a modular and extensible framework, enabling seamless experimentation and benchmarking across retrieval pipelines. It offers comprehensive documentation, open-source implementation, and pre-built evaluation tools, making it a powerful resource for researchers and practitioners in the field.

github

: 188

Eco2AI

Eco2AI is a python library for CO2 emission tracking that monitors energy consumption of CPU & GPU devices and estimates equivalent carbon emissions based on regional emission coefficients. Users can easily integrate Eco2AI into their Python scripts by adding a few lines of code. The library records emissions data and device information in a local file, providing detailed session logs with project names, experiment descriptions, start times, durations, power consumption, CO2 emissions, CPU and GPU names, operating systems, and countries.

github

: 245