🔍 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)