Adaion provides an AI-powered copilot for electric utility grid planning and operations. The platform builds high-fidelity digital twins by integrating GIS and AMI data to ensure accurate network models. This intelligence delivers actionable insights, automates compliance reporting, and generates optimized, scenario-based solutions for grid performance.
Funding
$2.8M 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.
IPFounders
Product
Problem
Energy grid operators face challenges in optimizing resource management and enhancing decision-making due to fragmented data sources and a lack of real-time visibility into grid operations. Inefficient processes and manual calculations hinder the ability to adapt to the evolving energy landscape and integrate renewable energy sources effectively.
Solution
Adaion offers a cloud-based platform that digitalizes energy grids by integrating data from various sources to create a digital twin, enabling real-time monitoring and forecasting through AI. The platform provides grid operators with a unified view of their medium voltage (MV) and low voltage (LV) networks, facilitating informed decision-making and optimized resource allocation. By digitizing measurements and enriching data, Adaion's solution automates processes, reduces management time, and enhances the ability to predict grid behavior. The digital twin emulates network behavior, allowing for advanced analytics and proactive management of grid congestions and potential issues.
Target Audience
The primary target audience includes energy grid operators and utility companies seeking to optimize grid management, integrate renewable energy sources, and enhance decision-making through real-time data and AI-driven insights.
Features
- Integration of data from diverse sources using connectors for interoperability.
- Digitalization of measurements with data debugging and enrichment to create a unified model.
- Real-time visualization of MV and LV network status for informed decision-making.
- Automation of processes to reduce manual calculations and management time.
- AI-powered forecasting to predict grid behavior.
- Digital twin creation to emulate network behavior.
- Congestion modeling and forecasting using AI techniques like deep learning.
- Power flow analysis (balanced and unbalanced) over the digital twin.
- Tools for assessing new prosumer connections based on technical analysis and historical data.
- Modules for solar feed-in detection, loss detection, and data integration monitoring.