Gradyent offers a real-time Digital Twin platform that creates a virtual model of heating grids, enabling operators to optimize temperature, dispatch, and system performance while integrating renewable energy sources. This technology helps heating companies reduce CO2 emissions, lower operational costs, and enhance decision-making through accurate simulations and predictive analytics.
Funding
$12.3M 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
District heating systems often lack comprehensive, real-time visibility and control, leading to inefficiencies in temperature management, suboptimal dispatch of energy sources, and difficulties in integrating renewable energy. Traditional software solutions offer only partial optimization, struggle with the complexity of large systems, and require costly manual updates.
Solution
Gradyent offers a real-time Digital Twin platform that creates a virtual representation of heating grids, enabling operators to optimize temperature, dispatch, and overall system performance. The platform simulates the entire grid, from production to consumption, allowing for the integration of renewable and low-temperature heat sources. By providing accurate simulations and predictive analytics, Gradyent helps heating companies reduce CO2 emissions, lower operational costs, and make smarter decisions regarding network design and operation.
Target Audience
Gradyent's primary customers are cities, heating companies, and industrial facilities that operate district heating grids and seek to improve efficiency, reduce emissions, and integrate renewable energy sources.
Features
- Real-time Digital Twin of the entire heating grid, from source to end-user
- Proprietary hydraulic solvers and optimizers designed for complex, large-scale systems
- Optimization of temperature, hydraulics, dispatch, and peak load in real-time
- Simulation capabilities for designing and evaluating future scenarios, including the integration of low-carbon sources
- Automated control with minimal operator effort, designed for seamless integration with existing systems
- Efficient data loaders and a SaaS model for an always up-to-date model