SDG 11: Smart Lighting to Reduce Energy Usage in Buildings
Saving the Earth with AI + the Internet of Things
Buildings are responsible for 27% of operational carbon emissions due to their energy usage. Connecting office building occupancy to lighting can cut that usage by as much as 40%. Motion detectors can reduce wasteful energy usage by illuminating buildings, floors, and spaces only when people are detected. Additionally, light sensors can adjust brightness based on the amount of natural sunlight present to save even more energy. Predicting when and where people will be within buildings with AI is even better.
IoT + AI Blueprint:
Assemble the collection of components displayed below to address the use case of reducing energy usage in buildings with smart lighting.
| Sensor(s) |
|---|
| Motion: Detect People |
| Light: Detect Intensity of Outdoor Light Entering Building Through Windows |
| Device(s) |
|---|
| Microcontrollers |
| Single Board Computer (If AC Power Available) |
| Power Options |
|---|
| Lithium-Ion Polymer (LiPo) Batteries |
| Solar |
| Power Over Ethernet (PoE) |
| AC Power (Utility | Mains | Wall Outlet) |
| Network(s) |
|---|
| Ethernet: If Present Within a Given Space |
| Wi-Fi: If Available for Indoor Scenarios |
| LoRa: Create Coverage Needed to Reach Internet Access |
| Cellular: If Indoor Coverage Available and Cost-Effective |
| Digital Twin Modeling |
|---|
| Buildings |
| Floors |
| Spaces (Offices, Conference Rooms, Common Areas, Hallways) |
| Data Processing + Storage Location(s) |
|---|
| Edge: In Building |
| Cloud: Filtered Data Relayed from Edge to Monitor More Than One Building or Property |
| Streaming Analytics |
|---|
| Compare Sensor Data Values to Defined Setpoints, KPI Value Ranges and Thresholds |
| Filter out Duplicate Sensor Data Value Readings |
| Automation |
|---|
| Green KPI: No People, Turn Off Lights. Outside Light Intensity Low, No Action. |
| Yellow KPI: Outside Light Intensity Medium, Dim Indoor Lights Slightly. |
| Red KPI: People Present, Turn On Lights. Outside Light Intensity High, Dim Indoor Lights More. |
| People |
|---|
| Deploy and Maintain Solution |
| SMEs Define KPIs and Actions |
| Facilities Management Personnel, Lighting Experts |
| Security |
|---|
| Uniquely Identify Each Device |
| TLS Encryption for Data in Transit |
| Encrypt Data at Rest |
| Validate Device Messages to Ensure they use Expected Data Format |
| Rotate Security Tokens |
| Limit IP Address Ranges |
| AI Anomaly Detection |
| Zero Trust: Reauthenticate Device Messages Through Every Step of the System |
| Artificial Intelligence |
|---|
| Machine Learning Time Series Forecasting Model to Predict When People are Most Likely to be in Buildings, Floors, or Specific Spaces |
| Machine Learning Time Series Forecasting Model to Predict When Various Levels of Outdoor Light Intensity will Affect Indoor Lighting |
| IoT Platform |
|---|
| Device SDK Captures Sensor Data from Physical Twin and Securely Sends it as a JSON Payload to the IoT Platform Along with a Unique Identifier and Security Token |
| IoT Platform Captures, Authenticates, and Saves Incoming Device + Sensor Data to a Message Queue |
| Background Process Takes Queued Data and Hydrates the Digital Twin by Saving it to a Database Table that Mimics the Structure of the Physical Twin |
| AI Model is Trained and Retrained with Digital Twin Dataset of Current and Historical Data |
| Hot Path Data is Sent to Streaming Analytics to Facilitate Real Time Alerting and Automation |
| Hot Path Data is Also Sent to AI Model for Inferencing to Predict Future Occurrences |
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March 6, 2025 at 08:16AM
Rob Tiffany