Energy efficiency doesn’t stop at design or retrofits – it thrives on continuous monitoring.
But many buildings still rely on manual audits, delayed fault detection, or incomplete metering. That’s where AI changes the game – making real-time, granular, and intelligent energy monitoring a reality for every building, from offices to campuses.
🎯 How AI Can Make This Product or Solution Much Better
🔍 Granular Energy Monitoring & Disaggregation
AI uses Non-Intrusive Load Monitoring (NILM) to break down total energy consumption into specific uses – HVAC, lighting, plug loads, equipment – without needing individual sensors for each.
This lets building managers see exactly where energy is going, revealing underperforming systems and usage anomalies.
Our specialty focus areas include
Net Zero by Narsi
Insights and interactions on climate action by Narasimhan Santhanam, Director - EAI
View full playlist📈 Automated Baseline & KPI Generation
AI generates dynamic baselines using historical data, weather inputs, and occupancy patterns – giving facility teams clear visibility into what “normal” looks like.
It calculates critical KPIs like Energy Use Intensity (EUI), load factors, and operational efficiency – all continuously updated.
🚨 Anomaly Detection & Fault Diagnosis
Machine learning models identify spikes, drifts, or irregular patterns in energy use – caused by faulty equipment, poor scheduling, or user error.
Real-time alerts and root-cause diagnostics allow teams to act before inefficiencies escalate into high costs or equipment damage.
👥 Occupancy-Aware Energy Insights
AI correlates energy use with occupancy data (from sensors, access logs, or scheduling systems) to distinguish essential vs. wasteful loads.
It flags underused zones with high consumption – fueling better space planning, zonal energy control, and cost recovery in shared spaces.
🌐 Portfolio-Level Intelligence for Multi-Site Operations
For portfolios like retail chains, industrial campuses, or government facilities, AI aggregates energy performance across sites.
It ranks buildings by efficiency, deviation from targets, and savings potential, guiding strategic investment decisions and ESCO performance contracts.
🛠️ How AI Overcomes Key Challenges
| Challenge | AI-Enabled Solution |
|---|---|
| No sub-metering or limited sensor infrastructure | NILM disaggregates loads using only main meter data |
| Static or outdated energy baselines | AI builds adaptive, real-time benchmarks for dynamic environments |
| Delayed or manual audits | Always-on monitoring with automated diagnostics and report generation |
| Difficulty linking behavior to energy waste | AI connects occupancy, scheduling, and consumption for actionable insights |
🤖 Main AI Tools and Concepts Used
- Non-Intrusive Load Monitoring (NILM)
- Unsupervised learning for anomaly detection
- Predictive analytics for peak load forecasting
- Clustering for behavior-based energy segmentation
- NLP tools for automated energy audit reports
📊 Case Studies
- Verdigris Technologies (USA):
Used AI + NILM to uncover hidden HVAC and lighting inefficiencies in commercial buildings, cutting energy use by 15–20% annually. - Tata Power EZ Home (India):
Integrated AI disaggregation into smart homes to optimize appliance-level energy use in real time.
🚀 Relevant Startups & Providers
| Company | TRL | Highlights |
|---|---|---|
| Verdigris Technologies | TRL 9 | Real-time NILM and deep learning for granular load insights in large buildings |
| Dexma (Spain) | TRL 9 | Cloud-based AI platform for energy monitoring, forecasting, and diagnostics |
| Wattics (Ireland) | TRL 8–9 | Commercial/industrial energy analytics with AI-driven efficiency modules |
| Ubiik (Taiwan) | TRL 8 | AI-powered AMI systems for sub-metering and energy disaggregation |
| Facilio (India/USA) | TRL 9 | Unified AI platform for operations, energy, and asset intelligence |
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