Rooftop solar power combined with battery storage has become the cornerstone of decentralized, clean energy. But to truly maximize efficiency, economics, and reliability, we need more than panels and lithium – we need AI. From sizing and design to operation and maintenance, AI is transforming how rooftop energy systems perform.
🎯 How AI Can Make This Product or Solution Much Better
⚡ Real-Time Energy Management
AI continuously analyzes solar production, battery charge level, weather forecasts, and household load to decide when to store, use, or export energy.
This improves self-consumption, reduces bills, and ensures backup power when it’s needed most.
📐 Sizing and Design Optimization
AI tools use satellite data, rooftop geometry, historical weather, and load profiles to recommend the ideal system size.
That means better ROI, lower upfront costs, and fewer mismatched installs.
🔋 Predictive Battery Control
AI learns usage patterns and battery health trends to fine-tune charging and discharging in real time.
This extends battery life, avoids inefficiencies, and maximizes the value of each cycle.
Our specialty focus areas include
Net Zero by Narsi
Insights and interactions on climate action by Narasimhan Santhanam, Director - EAI
View full playlist⏱️ Time-of-Use Optimization
AI schedules high-energy tasks and battery use around time-of-use (ToU) tariffs or dynamic pricing.
By shifting loads, it lowers utility bills and reduces strain on the grid.
🛠️ Fault Detection & Maintenance
AI spots shading issues, inverter faults, battery degradation, or soiling before they become big problems.
This keeps rooftop solar systems operating at peak performance with minimal intervention.
🛠️ How AI Overcomes Key Challenges
| Challenge | AI Solution |
|---|---|
| Intermittent generation + unpredictable load | AI balances supply, storage, and demand with real-time forecasts |
| Suboptimal system sizing | AI simulates usage and irradiance to right-size PV + battery |
| Battery degradation and loss of capacity | AI predicts wear and optimizes depth-of-discharge to extend lifespan |
| Low user engagement or system misuse | AI dashboards and gamification improve awareness and interaction |
🤖 Main AI Tools and Concepts Used
- Time-series forecasting for solar and load prediction
- Reinforcement learning for battery dispatch and energy scheduling
- Predictive analytics for component health and fault detection
- Edge AI for real-time local decisions
- Computer vision for aerial PV array design
📊 Case Studies
- SonnenBatterie (Germany): AI coordinates home batteries as part of virtual power plants across communities.
- Tigo Energy (USA): AI platform detects faults and optimizes rooftop PV + battery systems remotely.
🚀 Relevant Startups & Providers (TRL 8–9)
| Company | Focus |
|---|---|
| Tesla Energy (USA) | Integrated AI for solar + storage systems and grid-aware load balancing |
| Sonnen (Germany) | AI-managed home battery and community energy networks |
| Moixa (UK) | AI platform for battery + EV + solar control in residential buildings |
| Ferroamp (Sweden) | DC nanogrid system with AI-powered energy flow management |
| Tigo Energy (USA) | AI-based optimization and fault diagnostics for rooftop solar installations |
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