Comprehensive building energy management requires the submetering of all major electrical systems, circuits and equipment to drive better power efficiency. Using a single, overall electrical current meter for the whole building isn’t granular enough to discover inefficiencies. You start by creating a baseline of electricity usage throughout all parts of the building and then search for inefficiencies. Find areas of abnormally high energy consumption and then mitigate anomalies with repairs or more energy efficient equipment. This cuts energy usage, reduces emissions, and saves money. Predicting excessive energy usage within buildings with AI is even better.
IoT + AI Blueprint:
Assemble the collection of components displayed below to address the use case of driving better energy efficiency in buildings.
Sensor(s)
“Clamp On” Electrical Amperage Current Meter
Device(s)
Vutility Hotdrop
Adeunis Current Sensor
Power Options
Harvests Power Directly from the Line it Monitors via Inductive Charging
Network(s)
LoRa: Create Low Bandwidth Coverage Needed to Reach Internet Access
Digital Twin Modeling
Electrical Assets
Current Flow
Potential Replacement Equipment
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: Normal Electricity Usage, No Action.
Yellow KPI: Warn Notification for Awareness When Sensor Values are Trending Towards Excessive Electricity Usage
Red KPI: Alert Notification for Excessive Electricity Usage
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 Excessive Amounts of Electricity is Used
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 Where Properties of Twin are Mapped to ML Features
Hot Path Data is Sent to Streaming Analytics to Facilitate Real Time Alerting and Automation
Same Hot Path Data is Also Sent to AI Model for Inferencing to Predict Future Occurrences
Rob is a writer, teacher, speaker, world traveller and undersea explorer. He's also a thought leader in the areas of enterprise mobility and the Internet of Things.
