Client challenge
A renewable energy wholesaler operating its own PV farm and battery storage system was looking to automate energy production and storage management. The goal was to reduce energy loss and increase efficiency — the existing system relied on manual controls and reactive decision-making.
Objective
To develop an AI-driven integrated system that uses weather forecasts, consumption data, and energy tariffs to automatically optimize production and manage battery storage: charging and discharging based on profitability and predictive conditions.
Story
Following an infrastructure audit, a system was designed using predictive algorithms and integration of data from inverters, IoT sensors, weather APIs, and energy tariffs.
The AI analyzed historical consumption, production data, and real-time weather dynamics to intelligently manage charging cycles — selecting the most cost-effective moments (e.g., avoiding surplus energy being fed back to the grid at low rates).
A centralized analytics dashboard generated reports and alerts, giving operators insight to react in real time or define long-term optimization strategies.
Results (KPIs)
✅ Increased on-site energy self-consumption by 27%
✅ Reduced energy loss to the grid by 41%
✅ ROI achieved in just 2.3 years
✅ 94% system availability (no downtime in control logic)
✅ 100% automation of battery charging/discharging processes
Implementation summary
- Audit of historical data and IT infrastructure
- Design of predictive AI algorithms
- Integration with inverters, battery systems, and weather forecasting tools
- MVP development and simulation testing
- Full-scale deployment and performance optimization
Scope of implementation
- AI: forecasts for production, demand, and market prices
- IoT: data from PV inverters and battery storage
- EMS: automated energy storage management
- Analytics: performance reports, system alerts
- Energy market integration: real-time tariff and demand data
Key and supporting technologies
⚙️ Artificial Intelligence – optimization of charge/discharge decisions
⚙️ IoT – real-time data from physical devices (PV & battery)
⚙️ Big Data – consumption and weather data analysis
⚙️ Edge computing – local-level decision-making
⚙️ EMS/SCADA – device automation layer
Tech stack
- TensorFlow, scikit-learn (AI/ML)
- MQTT + Modbus (IoT communication)
- Node-RED + Grafana (monitoring & data visualization)
- Python (backend + integration)
- AWS (data analytics & storage)
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