AIESS Energy Core
Autonomous energy management system with conversational AI interface
- Elimination of price thresholds — multi-dimensional real-time optimization
- Optimization decisions every 15 minutes based on market data and ML predictions
- Edge–cloud architecture ensuring autonomy and reliability
Why are traditional EMS systems insufficient?
Traditional energy management systems rely on static price thresholds. This approach ignores market dynamics, consumption patterns, and battery degradation costs.
Traditional EMS
- Manual price thresholds — static "charge below X, discharge above Y" rules
- No consumption prediction — reactive instead of anticipatory
- No battery cycle cost accounting — accelerated degradation
- Decisions once daily — no adaptation to intraday changes
AIESS Energy Core
- ML load prediction — model trained on historical consumption patterns
- Degradation cost as a decision variable — every cycle has its price
- Day-ahead + intraday market integration — complete energy pricing picture
- Decisions every 15 minutes — continuous real-time optimization
Layered Architecture
The system is built on a multi-layered architecture where each layer handles a distinct scope of responsibility.
Hardware Layer
Physical energy storage layer
- LFP battery modules with liquid cooling
- Hybrid inverters
- Sensors and BMS systems
Edge Layer
Local processing and control
- Edge controller with fail-safe logic
- Offline data and decision buffering
- Modbus / CAN / RS485 communication
AIESS Energy Core
Optimization core — AI decision engine
- Multi-variable optimization solver
- ML models: consumption, PV output, and price prediction
- Market data API (day-ahead, intraday, TGE)
AIESS Platform
Fleet management and analytics platform
- Real-time operator dashboard
- Multi-site installation management
- ROI reports and historical analysis
Conversational AI Interface
Natural language communication
- Natural language queries about storage status
- On-demand report generation
- Optimization decision explanations
How Energy Core Works
The decision engine processes data from multiple sources and generates an optimal storage schedule every 15 minutes.
Day-ahead & intraday price analysis
Fetching and analyzing prices from the Day-Ahead Market and the intraday market in real time.
Consumption prediction (ML)
Machine learning model forecasts the energy consumption profile based on historical data, weather, and calendar.
PV generation prediction
Solar generation forecast based on weather data and installation characteristics.
Battery cycle cost
Every charge/discharge decision accounts for battery degradation cost as an optimization variable.
Import/export constraints
Respecting grid connection power limits, distribution agreements, and network constraints.
Power buffer
Maintaining energy reserves for peak shaving and operational safety requirements.
Optimization every 15 min
The solver generates a new schedule every 15 minutes, adapting to changing market conditions.
Edge–cloud architecture
Decisions made locally on edge with cloud synchronization. Full autonomy during connectivity loss.
From single installation to cluster
AIESS Energy Core scales from a single storage unit to a centrally managed fleet.
Single storage unit
Single installation optimization — ideal for SMEs and business prosumers. Full local autonomy.
Industrial cluster
Coordination of multiple storage units at a single location. Joint optimization respecting network constraints.
Storage fleet / VPP
Centralized management of distributed storage assets. Future-ready: Virtual Power Plant (VPP) and balancing market participation.
A system that learns your energy
AIESS Energy Core uses machine learning to model the energy consumption profile of a specific site. Based on historical data, it forecasts demand, identifies recurring patterns and adapts the battery operation strategy accordingly.
Site-specific ML load forecasting
Adapts to seasonality, weekends and production changes
Detects anomalies vs baseline profile
Improves buffer and peak-power decisions
Economic Impact
Average day-ahead market spread
Investment payback period
Optimization decision frequency
Multi-dimensional optimization instead of price thresholds — maximizing profit while protecting battery lifespan.
Why AIESS Energy Core?
SME and industrial deployments
System designed and tested in real operational conditions of the Polish energy market.
Edge–cloud architecture
Local reliability with full cloud analytics. No single point of failure.
Autonomous operation mode
The storage makes decisions independently — no operator, no manual schedules, no downtime.
Natural language communication
Conversational AI interface allows operators and managers to ask questions and receive answers in natural language.
Data Security & Sovereignty
All operational and analytical data is stored exclusively on servers located within the European Union. No data leaves the European infrastructure.
100% European infrastructure
Servers, databases, and processing services — all located in EU data centers, fully compliant with GDPR and European security standards.
Energy and digital independence
Critical energy management data remains under European jurisdiction. No dependency on non-European infrastructure.
Edge-level autonomy
The edge controller operates locally even without cloud connectivity. Sensitive data does not need to leave the client premises.
End-to-end encryption
Communication between system layers is encrypted. Data access requires authentication at every level of the architecture.
Product submitted for ENEX AWARD 2026