Energy data such as asset information and energy contracts is often not digitized or stored in an unstructured way. At the same time it is scattered across different departments, IT systems and Excel spreadsheets – which hampers transparency, collaborative decisions and the use of AI agents.
Optimize costs
Reduce levelized energy cost per unit through data-driven decisions and load management.
Decide based on data
Create a single source of truth for all energy-relevant data – usable across departments.
AI-ready energy data
Structured, validated energy data as the foundation for AI agents and algorithms.
The building blocks of the Digital Twin
All the features you need to fully understand and optimize your energy system.
Request a demoUnified modeling of all energy carriers: electricity, gas, heat, cooling, compressed air and steam. Cross-carrier optimization for holistic energy cost analysis – from spot market arbitrage to compliance with the German Heat Planning Act.
Portfolio-compliant storage of trading products: futures, PPAs, spot market contracts. All procurement contracts captured in a structured way for day-ahead, intraday and long-term hedging. Integration of PPA options and flexibility marketing.
Complete modeling of your grid connections with capacity limits, grid fees and levies. Separation of energy trading prices and grid costs for precise scenario analysis – including grid expansion or transformer expansion.
Detailed modeling of all energy components such as: CHP plants, PV, heat pumps, e-boilers, gas turbines, heat storage and batteries. Including efficiencies, operating points and availabilities for optimal dispatch planning.
Detailed modeling of production machines and demands in order to make more accurate demand forecasts and to analyze the potential of Production Scheduling.
Automatic detection and correction of outliers, data gaps and measurement errors. Plausibility checks against physical limits.
Full traceability of all changes to the model. Every parameter change is logged – for audit security, the four-eyes principle and efficient teamwork. All changes are logged and can be viewed at any time.
Configurable thresholds and automatic notifications via email or push. Automated reports for management and audits. Role-based dashboards for every audience.
Frequently asked questions
Answers to the most important questions about implementation and use.
More questions?Implementation takes place in five phases: (1) Use case identification – based on the use case table we identify which data needs to be integrated in order to reduce your energy cost per unit. (2) Data collection – all relevant data is collected specifically for the identified use cases. (3) Model build – building the energy model with information from the real system. (4) Validation – verification based on data the model has not yet seen, until historical performance is reproduced exactly. (5) Go-live – after successful validation, connection to control systems and activation. Typical go-live time: 4–6 weeks.
For missing data such as investment costs for certain components, our base data can be used as a foundation. Beyond that, our solution engineering team can help fill in missing data with additional internal models – for example for heating demands – and practical experience.
The scalable solution complements existing IT systems consisting of control technology, energy management and data hubs. Standardized interfaces enable connection to ERP systems, control technology and other data sources. The Digital Twin acts as a central data base that consolidates all energy-relevant information.
The Digital Twin supports various data formats for maximum flexibility. Time series data can be fed in via CSV upload or live API integration. Separate CSV files can be used for different components. Important: energy data such as natural gas must be provided in energy units (kWh or MWh), not in volume units such as cubic meters.
Live data integration can be done in several ways: via APIs to central systems such as data lakes or via an edge controller. In addition, we support direct communication with smart meters and PLC systems via industrial protocols such as Modbus, OPC UA and EDC. For intraday trading we recommend 15-minute intervals. The data is used exclusively for the numerical optimization of your specific energy system – not for training general AI models.
The Digital Twin offers multi-site capability and can model from Germany-wide aggregation down to the individual asset. All energy carriers are supported: electricity, gas, heat, cooling, compressed air and steam. In addition, production data such as shift schedules and production programs can be integrated to link energy optimization with production planning.
We offer a structured training program: self-learning videos for self-study and guided workshops for practical application. Our "train the trainer" model enables your team to maintain and extend the model independently. Regular project check-ins and a dedicated support channel ensure continuous support throughout the entire usage.
Data security is our highest priority. We hold relevant certifications, including TISAX and ISO 27001. All data processing agreements are carefully documented. For automation we take a step-by-step approach with parallel operation, so you can build trust in the system before critical processes are automated.
Yes, the Digital Twin is designed as a complement to existing IT systems – including control technology and energy management systems. Depending on your requirements, integration can be done as human-in-the-loop with recommended actions or as automatic control. For the IT-OT bridge, an edge controller is available that ensures secure communication between your systems.
Yes, that is possible. The same Digital Twin can be used for both strategic design and investment planning as well as for operational operation. For Operation Hub mode the model is parameterized in more detail – for example with ramping constraints and minimum load requirements – and integrated with real-time data.
Yes, the Digital Twin enables complete modeling of your grid connections with capacity limits, grid fees and levies. The expansion of grid connection capacity can be parameterized, including the cost per MW for the expansion. This lets you precisely analyze scenarios for transformer expansions or grid expansion.
Yes, the Digital Twin offers holistic optimization that combines peak shaving with total energy consumption. The system dynamically evaluates the trade-offs between load peak reduction and spot market arbitrage. This way battery storage is not only optimized for peak shaving, but also for using cheap electricity prices – with the goal of minimizing total energy costs.
