The startup develops distribution management software that employs machine learning techniques based on artificial neural networks to accurately estimate the state of power grids using only ten percent of the processing power required by traditional methods. This enables distribution network operators to achieve real-time grid awareness and ensure safe, efficient operations at a lower cost.
Funding
$590K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Distribution network operators face challenges in achieving real-time grid awareness due to the high computational costs and extensive sensor infrastructure required by traditional grid state estimation methods. This lack of comprehensive visibility hinders efficient grid management and can compromise the safety and reliability of power distribution.
Solution
Gridhound offers a distribution management software suite, Graice, that leverages patented AI algorithms to provide accurate grid state estimation using minimal processing power and a reduced number of sensors. This enables distribution network operators to gain real-time insights into grid conditions, optimize network operations, and ensure the safe and efficient distribution of power. The software suite facilitates the digitalization of the power distribution grid by providing data-driven solutions for data quality management, analytics, planning, and control.
Target Audience
The primary target audience includes distribution network operators seeking to enhance grid visibility, optimize operations, and facilitate the integration of renewable energy sources.
Features
- AI-based algorithms for real-time grid state estimation with reduced sensor requirements
- Data Quality Management module for creating consistent and load-flow capable CIM network models
- Measurement Data Management module for consolidating, preparing, and evaluating measurement data from various sources
- Optimal sensor placement module combining engineering approaches with AI algorithms
- Power forecasting for renewable energy plants and local substations using historical and weather data
- Load forecasting for network areas based on real-time monitoring and power predictions
- Energy management system for flexible generators and consumers, enabling both grid-support and self-consumption optimization
- Simulation platform for scenario analysis and planning, integrating all Graice modules