Wedoco builds high‑fidelity digital twins of power plants, grids, and building energy systems using open‑source tools like Modelica and BOPTEST. These twins enable predictive analysis, scenario testing, and advanced control (model predictive control, reinforcement learning) to improve efficiency, reduce costs, and support decarbonization. Wedoco also offers consultancy, research collaborations, and training to help utilities, grid operators, industrial energy managers, and research institutions implement and operate these models.
Funding
Funding not disclosed
Founders
Product
Problem
Energy system operators and planners often lack accurate, real‑time models that can predict system behavior under varying conditions, leading to suboptimal performance, higher costs, and reduced sustainability.
Solution
Wedoco creates high‑fidelity digital twins of energy infrastructures using open‑source modeling tools such as Modelica and BOPTEST. These twins enable predictive analysis, scenario testing, and advanced control strategies—including model predictive control and reinforcement learning—to identify efficiency improvements before they are implemented in the physical system. The company complements the technology with consultancy services that tailor models to specific assets, research collaborations that push the state of the art, and training programs that equip client teams with the skills to develop and operate their own digital twins. By delivering actionable insights and a clear pathway to smarter, more sustainable energy operations, Wedoco helps clients reduce operational costs and accelerate decarbonization goals.
Target Audience
Primary customers are utilities, grid operators, industrial energy managers, and academic or research institutions seeking to improve the efficiency and reliability of their energy systems.
Features
- Development of custom digital twin models for power plants, grids, and building energy systems using Modelica and BOPTEST
- Integration of advanced control algorithms such as model predictive control and reinforcement learning for performance optimization
- End‑to‑end simulation workflows that support scenario analysis, forecasting, and what‑if studies
- Consultancy packages that include model validation, data integration, and deployment guidance
- Collaborative research projects delivering cutting‑edge methodologies and publications
- Tailored training courses on Modelica programming, software development, and advanced control techniques