Project Ideas for Edge AI in Power/Energy Industry: Smart Meter and Smart Grid
📊 Project Idea 1: AI-Based Non-Intrusive Load Monitoring (NILM) in Smart Energy Meters
🎯 Goal:
To develop an Edge AI system embedded in a smart meter that identifies and classifies appliance-level energy consumption without needing individual sensors — enabling load disaggregation, energy awareness, and user-level insights for demand response.
🤖 How Edge AI Helps:
Edge AI models analyze the aggregate power signal (voltage, current, power factor) and extract signatures of different appliances (e.g., fridge, AC, heater). By running inference locally, the meter can provide real-time disaggregated feedback without high-bandwidth cloud transmission or privacy issues.
📥 System Requirements – Input Values Needed
| Sensor / Value | Source | Purpose |
|---|---|---|
| Voltage & current waveforms | Smart meter ADCs | Detect appliance switching signatures |
| Power factor, apparent power | Meter DSP block | Identify type of appliance (resistive/inductive) |
| Harmonics / FFT coefficients | Edge DSP or preprocessed FFT | Extract unique device patterns |
| Event timestamping | Internal RTC | Analyze behavior over time |
🧠 AI Model Used
| Functionality | AI Model Type |
|---|---|
| Load Disaggregation (NILM) | 1D-CNN or LSTM-AE on power traces |
| Appliance Classification | Random Forest / Gradient Boosted Trees |
| Event Detection (On/Off changes) | Change Point Detection + Sequence Classifier |
| Adaptive Learning per User | Online Learning + Personalization Layer |
⚡ Project Idea 2: Real-Time Demand Forecasting & Grid Load Balancing via Smart Meters
🎯 Goal:
To deploy Edge AI models on smart meters (or concentrators) that predict localized demand at the household or transformer level and communicate balancing suggestions to the grid operator for dynamic load management and peak shaving.
🤖 How Edge AI Helps:
By locally forecasting 15 to 30-minute load profiles, smart meters act as micro-intelligence units that feed aggregated demand forecasts upward. This enables distributed load shaping, battery coordination, and better grid stability in areas with high EV/PV penetration.
📥 System Requirements – Input Values Needed
| Sensor / Value | Source | Purpose |
|---|---|---|
| Active & reactive power (real-time) | Energy meter core | Base input for forecasting |
| Time-of-day & date | RTC or GPS sync | Temporal pattern recognition |
| Weather data (temp, cloud cover) | Edge-cached API or sensor | Adjust for cooling/heating loads |
| Historical usage | Flash storage or head-end sync | Train baseline demand model |
🧠 AI Model Used
| Functionality | AI Model Type |
|---|---|
| Short-term Load Forecasting | LSTM / GRU or Prophet |
| Clustered Consumer Profiling | K-Means + GBT Ensemble |
| Transformer Load Aggregation | Federated Learning Architecture |
| Demand Response Triggering | Event Classifier + Control Rule Engine |
🔐 Project Idea 3: Energy Theft and Tamper Detection using Edge AI on Smart Meters
🎯 Goal:
To create an on-device AI engine within smart meters that detects anomalies indicating energy theft or tampering, such as meter bypassing, current inversion, or communication spoofing — enabling instant flagging at the edge without reliance on central analysis.
🤖 How Edge AI Helps:
AI models learn the normal energy consumption profile and detect subtle anomalies in pattern, phase relationships, and waveform distortion. Since these patterns vary from one consumer to another, a local model is more personalized and adaptive, and harder to fool than static rules.
📥 System Requirements – Input Values Needed
| Sensor / Value | Source | Purpose |
|---|---|---|
| Energy usage (kWh/kVAh) | Smart meter core | Consumption trend monitoring |
| CT/PT signal quality | Voltage & current sensors | Detect tampering or bypass |
| Meter event logs | Internal meter flash | Log unauthorized resets, open cover, etc. |
| Communication packet metadata | DLMS/OCPP packet logs | Analyze protocol spoofing or replay |
| Reverse power flow detection | Bidirectional metering logic | Spot illegal injection to grid |
🧠 AI Model Used
| Functionality | AI Model Type |
|---|---|
| Anomaly Detection for Theft | Autoencoder / Isolation Forest |
| Behavior Drift Monitoring | LSTM trained on seasonal profiles |
| Signature Pattern Matching | SVM / Decision Tree on known tamper signals |
| Communication Intrusion Detection | RNN or LSTM for protocol log analysis |
