Energy Intelligence // EI
The intelligence that turns storage into infrastructure.
EMS, AI-supported optimisation and data-driven system orchestration form the software layer that controls the system across hours, days and seasons.
Hardware stores. Software earns.
The economic and operational performance of a DMES plant does not come from storage and components alone, but from the software intelligence that controls the system across several time horizons. eRevo groups this layer under the term Energy Intelligence, the interplay of a classic energy management system (EMS), AI-supported optimisation and data-driven system orchestration.
Not a feature. The connecting piece.
Energy Intelligence is not a downstream add-on but the actual link between generation, storage, thermal use and grid interaction. This layer decides continuously when the system charges, stores or feeds back, and in doing so connects the physical plant with the markets in which it earns.
Four layers, one architecture.
01
Forecast-based control
Weather, load and price forecasts over 24 to 72 hours are processed continuously and translated into anticipatory charging and discharging decisions. The system acts before demand arises.
02
Multi-asset optimisation
Electricity, heat, hydrogen, photovoltaics and grid supply are coupled in a single joint optimisation, no isolated battery control, but an integrated energy flow logic across the entire system.
03
Grid services
Peak shaving, response to dynamic grid tariffs and balancing within vZEV and LEG structures. Control reserve is possible as a supplementary revenue stream, but never the basis of profitability.
04
Self-learning operation
An AI operating layer based on open large language models is integrated into ongoing plant operation. It continuously evaluates operating data, detects anomalies and adapts control parameters across the entire life cycle of the plant.
Every plant learns. And every new one starts with the knowledge of all those before it.
Operating data flows from system to system over the years. This combination of multi-asset optimisation, LLM-supported operations management and multi-year transfer of learning data is a technological lead that goes beyond the hardware alone and cannot be replicated in the short term.
Rooted in the core team, not bought in.
Software leadership from day one.
CEO Gregor Zust brings many years of leadership experience in software development and is responsible for the strategic direction of the Energy Intelligence layer.
Industrial EMS experience.
As CEO of pi-System GmbH, CTO Bernhard Sax previously led the development of industrial energy management systems, the technical backbone of the EI architecture.
Developed further from data.
Energy Intelligence is developed and continuously refined as a core technology by eRevo, together with analytics partner Qynn Energy, which contributes the economic assessment and ROI modelling of projects.
What does intelligence earn at your site?
The potential analysis does not work with rules of thumb; it simulates your system across every level, from load profiles and PV yield to heat and markets. The Quick Check is the first step.
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