Problem
Energy-intensive industries — such as chemical plants, cement factories, and foundries — are under pressure from rising energy prices and the need to improve efficiency. Most facilities still rely on static schedules and manual data analysis to control consumption, resulting in energy waste and missed savings opportunities.
Meanwhile, many of these sites are deploying renewables (like PV) and battery energy storage systems (BESS), but lack intelligent tools to coordinate generation, storage, and consumption in real time.
Goal
Reduce energy costs and increase operational flexibility through automated energy management — using AI-powered forecasting, process optimization, and dynamic control of PV and BESS resources.
Story
A large chemical manufacturing plant in Central Europe faced soaring electricity bills and limited visibility into its energy use. Energy-intensive systems like reactors and chillers operated based on rigid production schedules — ignoring real-time electricity prices or on-site solar generation.
The company implemented an integrated automation and energy optimization solution based on:
- Industrial SCADA/PLC systems (e.g. Siemens S7)
- IoT sensors (temperature, consumption, pressure)
- A local battery storage system (BESS)
- Predictive algorithms and AI-based control logic
The AI continuously analyzed operational data, weather forecasts, energy tariffs, and PV output to:
– dynamically shift consumption away from peak-price hours,
– charge/discharge BESS at optimal times,
– and run processes when grid and market conditions were most favorable.
The system also visualized KPIs on a real-time dashboard used by both plant operations and executive teams — enabling faster, data-driven decisions.
In the next phase, the site plans to connect with national grid flexibility programs and energy trading platforms.
Results (KPI)
✅ 19% reduction in average energy costs
✅ 35% increase in PV and BESS utilization
✅ 28% drop in peak-hour grid usage
✅ ROI achieved in just 2 years
✅ Real-time responsiveness to energy price fluctuations
🌱 Higher energy efficiency and measurable reduction in CO₂ emissions
💡 Recognized internally as a model project to replicate across other facilities in the group
Implementation Summary
- Energy audit of SCADA/PLC, PV, BESS systems
- Installation of additional IoT sensors (temperature, pressure, consumption)
- Integration with weather APIs and market price data
- ML model training for consumption forecasting and process optimization
- Automated control of BESS and high-energy equipment
- Dashboards with KPIs for operations and management
Scope of Work
- Real-time optimization of industrial energy use
- Smart control of chillers, furnaces, and reactors
- AI-based BESS charge/discharge scheduling
- Full integration with SCADA, PLC, and EMS
- Edge + Cloud architecture for local control and centralized analytics
📦 Scalable deployment model — ready for rollout to other production sites
Technologies
⚙️ SCADA / PLC – Siemens S7, Wonderware, Ignition
⚙️ EMS – Local energy management with PV + BESS integration
⚙️ IoT / Edge – Real-time data capture + processing (e.g. Nvidia Jetson)
⚙️ AI / ML – Forecasting and intelligent process optimization
⚙️ Big Data / Cloud – Market, weather, and historical analytics via Azure/AWS
