May 01, 2026
AI-powered platform and IoT infrastructure for distributed energy resources
Significant Grid Users (SGUs) needed more than historical data to manage complex energy portfolios, navigate volatile market prices and meet sustainability targets — they needed a live, context-aware operating layer.
Energy cost & CO₂ footprint
↓↓
Duration
16 weeks
Team
5 people
Challenge
SGUs increasingly need advanced tools to manage complex energy portfolios, navigate fluctuating market prices and meet strict sustainability goals. Relying on historical data is no longer sufficient to optimize costs and reduce carbon footprints effectively.
Solution
We designed and implemented an end-to-end, AI-powered platform supported by a robust IoT infrastructure. The system continuously aggregates real-time energy consumption data directly from proprietary IoT field devices. To provide full contextual awareness, the platform integrates via APIs with external data sources, capturing live signals such as weather forecasts, solar irradiance, wind conditions and regional CO₂ grid emissions based on the current generation mix.
Outcome
The platform's analytical engine processes this complex data matrix to generate a continuous, actionable value stream for the end-user. SGUs dynamically optimize their operations to significantly reduce both energy costs and environmental footprint, and leverage precise data profiles to strengthen tariff negotiations, accurately select contracted capacity and optimize capacity fees (opłata mocowa) — avoiding penalties and overpayment.
What was happening
Significant Grid Users were running portfolios where energy was a top operating cost, sustainability commitments were tightening and market prices moved on intraday timescales — but the decision layer still ran on historical reports.
Off-the-shelf BMS and EMS tools captured consumption, but none of them stitched together the contextual signals (weather, irradiance, grid CO₂ intensity, market price) that actually drive optimal operating decisions.
What changed
We designed and shipped a full operating stack: proprietary IoT field devices streamed real-time consumption data, API integrations brought in weather, solar irradiance, wind and regional grid-mix CO₂ signals, and an AI analytical engine fused the inputs into a continuous, decision-grade signal.
The output stream let operators dynamically reshape consumption around the cheapest and cleanest hours, and gave the commercial team precise profiles to negotiate tariffs, size contracted capacity and manage capacity fee exposure (opłata mocowa) — turning a cost center into a managed position.
- Proprietary IoT devices streaming real-time consumption telemetry
- API integrations: weather, irradiance, wind, regional CO₂ grid mix
- AI analytical engine fusing telemetry and context into a live signal
- Capacity fee (opłata mocowa) optimization and tariff negotiation data
AI Transformation, Process Optimization & Cost Efficiency