🔍 Key Use Cases & Problems Solved by Edge AI for Smart Meters
1. Load Disaggregation (Non-Intrusive Load Monitoring – NILM)
Problem: Consumers and utilities lack visibility into individual appliance consumption.
AI Model:
- Sequence models (LSTM, GRU)
- Convolutional Neural Networks (CNNs)
- Autoencoders
How it helps:
AI identifies the unique power signatures of individual appliances (AC, fridge, heater) from total consumption. This allows:
- Detailed energy insights for users
- Personalized energy-saving recommendations
- Demand-side management by utilities
2. Anomaly & Tamper Detection
Problem: Energy theft, meter tampering, and abnormal usage patterns are common.
AI Model:
- Isolation Forests
- One-Class SVM
- Autoencoders for anomaly detection
How it helps:
AI models can flag deviations from normal consumption patterns, such as:
- Sudden drops despite high appliance use
- Signal tampering or bypassing
- Bi-directional energy flow (in unauthorized setups)
3. Predictive Maintenance of Grid Components
Problem: Undetected degradation in transformer or line health causes blackouts.
AI Model:
- Time-series forecasting models (ARIMA, Prophet, LSTM)
- Random Forests for classification
How it helps:
By analyzing real-time voltage/current waveform irregularities:
- Detect transformer overloading early
- Predict equipment failure
- Trigger maintenance alerts to utilities
4. Load Forecasting & Demand Response
Problem: Grids face unpredictable peaks; poor planning leads to outages or expensive peaker plants.
AI Model:
- Recurrent Neural Networks (RNNs)
- Decision Trees & Gradient Boosted Trees
- XGBoost, LightGBM
How it helps:
- Short-term demand prediction (hourly/day-ahead)
- Automated demand response (e.g., turning off water heaters remotely during peak)
- Energy pricing optimization (dynamic tariffs)
5. Power Quality Event Detection
Problem: Poor power quality (sags, harmonics, transients) degrades appliance health.
AI Model:
- Signal processing + CNNs
- Event classification using SVM or DNNs
How it helps:
- Real-time detection of disturbances (sag, swell, flicker, THD)
- Trigger mitigation strategies (e.g., isolating loads)
- Send alerts to consumer or grid operator
6. Fraud Detection in Prepaid Meters
Problem: Prepaid energy meters are vulnerable to fraud (code injection, bypass, reverse polarity)
AI Model:
- Classification models (Random Forests, Logistic Regression)
- Graph Neural Networks (for distribution network behavior)
How it helps:
- Flag unexpected energy use after recharge
- Detect location or wiring inconsistencies
7. Consumer Behavior Analytics
Problem: Utilities need deeper understanding of usage patterns for targeting subsidies, energy programs, or solar compatibility.
AI Model:
- Clustering (K-Means, DBSCAN)
- Bayesian Networks
- Reinforcement Learning for recommendation systems
How it helps:
- Segment consumers (e.g., day vs night-heavy users)
- Personalized savings tips
- Cross-selling of energy products (e.g., solar panels, batteries)
