How-To Guides

Use Digital Twins to Contextualize Data

22min

Refer to the following how-to guide for leveraging Digital Twins for energy monitoring. 
User Scenario
In this user scenario, the devicehub device will simulate the energy monitoring process across different plant sites. The data generated by the tags reflects real-time energy production, which allows for active analysis of energy efficiency, cost, and consumption patterns.
Digital Twins will model the asset as an energy-monitoring machine located at several plant sites. The instances for the model will be the specific energy-monitoring machines at each site. 
The power generated in the last hour will be used to calculate values for cost, energy used, and energy rate. The timestamp value will be used to generate values for the attributes of season, start hour, and energy-rate description. 
We need to add two tags for this. Tag 1, named "L3_Machine2", will generate random values simulating the variable power of an energy-monitoring machine. Tag 2 in Devicehub collects and stores data from energy monitoring devices. It helps in analyzing energy rates and seasons by simulating timestamp values.
Step 1: Add a Device
Follow the steps to Connect a Device﻿ and configure the following parameters:
Device Type: Simulator
Driver Name: Generator
Enable Alias Topics: Select the checkbox. 
Step 2: Add Tags
After connecting the device, add the following tag. See Add Tags﻿ to learn more. 
Tag 1: L3_Machine2
Name: Select S - Random value generator
Value Type: Select float64
Polling Interval: Enter 1
Tag Name: Enter L3_Machine2
Min value: Enter 1
Max value: Enter 25
Values for power generated will be sent through the topic devicehub.alias.P2_Electrical_Panel.L3_Machine2. 
Tag 2
The data collected from Energy Monitoring devices is stored in Devicehub by enabling the Enable Data Store ﻿option on the simulator devices. The simulator device will act as a device measuring power values. The tag will simulate these timestamp values from one of the energy monitoring machines, and then the output will be written to the topic flowToDT.EMD_L3_Machine2.
To process this data, you will need to connect to a third-party database and create a Flow using Flows Manager. This flow will collect the values and determine the season and energy rate based on the date and timestamp.
JS
// Determine season and energy rate based on date and timestamp

var arr = msg.payload;
var arrOut = [];

var time = flow.get("tStart");
var tStart = new Date(flow.get("tStart"));
var month = tStart.getMonth();
var hour = tStart.getHours();
flow.set("month", month);
flow.set("hour", hour)

// Determine season
if (month > 5 && 10 > month){
    var season = 'Summer';
} else {
    var season = 'Winter';
}

// Determine energy rate
if (season == 'Summer'){
    if (hour >= 7 && 11 > hour || hour >= 17 && 19 > hour){
        var energyRate = 10.2;
        var energyRateDescription = "Mid-Peak";
    }
    else if (hour >= 11 && 17 > hour){
        var energyRate = 15.1;
        var energyRateDescription = "Off-Peak";
    }
    else {
        var energyRate = 7.4;
        var energyRateDescription = "On-Peak";
    }
}

else {
    if (hour >= 7 && 11 > hour || hour >= 17 && 19 > hour){
        var energyRate = 15.1;
        var energyRateDescription = "On-Peak";
    }
    else if (hour >= 11 && 17 > hour){
        var energyRate = 10.2;
        var energyRateDescription = "Mid-Peak";
    }
    else {
        var energyRate = 7.4;
        var energyRateDescription = "Off-Peak";
    }
}

for (let device = 0; device < arr.length; device++){
    
    obj = {}
    
    // Fields for SQL insert
    obj.time = time;
    obj.plant = arr[device].plant;
    obj.line = arr[device].line_tag;
    obj.asset = arr[device].name_tag;
    obj.shift = global.get("shift");
    obj.energyUsed = parseFloat((arr[device].integral).toFixed(2));
    obj.energyRate = energyRate;
    obj.energyRateDescription = energyRateDescription;
    obj.season = season;
    obj.cost = parseFloat((obj.energyUsed * energyRate).toFixed(2));

    arrOut.push(obj)
}

msg.payload = arrOut;

return msg;
// Determine season and energy rate based on date and timestamp

var arr = msg.payload;
var arrOut = [];

var time = flow.get("tStart");
var tStart = new Date(flow.get("tStart"));
var month = tStart.getMonth();
var hour = tStart.getHours();
flow.set("month", month);
flow.set("hour", hour)

// Determine season
if (month > 5 && 10 > month){
    var season = 'Summer';
} else {
    var season = 'Winter';
}

// Determine energy rate
if (season == 'Summer'){
    if (hour >= 7 && 11 > hour || hour >= 17 && 19 > hour){
        var energyRate = 10.2;
        var energyRateDescription = "Mid-Peak";
    }
    else if (hour >= 11 && 17 > hour){
        var energyRate = 15.1;
        var energyRateDescription = "Off-Peak";
    }
    else {
        var energyRate = 7.4;
        var energyRateDescription = "On-Peak";
    }
}

else {
    if (hour >= 7 && 11 > hour || hour >= 17 && 19 > hour){
        var energyRate = 15.1;
        var energyRateDescription = "On-Peak";
    }
    else if (hour >= 11 && 17 > hour){
        var energyRate = 10.2;
        var energyRateDescription = "Mid-Peak";
    }
    else {
        var energyRate = 7.4;
        var energyRateDescription = "Off-Peak";
    }
}

for (let device = 0; device < arr.length; device++){
    
    obj = {}
    
    // Fields for SQL insert
    obj.time = time;
    obj.plant = arr[device].plant;
    obj.line = arr[device].line_tag;
    obj.asset = arr[device].name_tag;
    obj.shift = global.get("shift");
    obj.energyUsed = parseFloat((arr[device].integral).toFixed(2));
    obj.energyRate = energyRate;
    obj.energyRateDescription = energyRateDescription;
    obj.season = season;
    obj.cost = parseFloat((obj.energyUsed * energyRate).toFixed(2));

    arrOut.push(obj)
}

msg.payload = arrOut;

return msg;
﻿
Sample Output Payload from Instance
This user scenario will be based on the following sample output payload from one instance. 
JSON
{
  "time": "2023-04-13T17:00:00.000Z",
  "plant": "Toronto",
  "line": "Line 1",
  "asset": "Machine 2",
  "shift": 2,
  "energyUsed": 2.51,
  "energyRate": 15.1,
  "energyRateDescription": "On-Peak",
  "season": "Winter",
  "cost": 37.9,
  "timestamp": 1681408544466,
  "startHour": "2023-04-13T17:00:00.000Z"
}
{
  "time": "2023-04-13T17:00:00.000Z",
  "plant": "Toronto",
  "line": "Line 1",
  "asset": "Machine 2",
  "shift": 2,
  "energyUsed": 2.51,
  "energyRate": 15.1,
  "energyRateDescription": "On-Peak",
  "season": "Winter",
  "cost": 37.9,
  "timestamp": 1681408544466,
  "startHour": "2023-04-13T17:00:00.000Z"
}
﻿
Step 3: Create Digital Twins Model
You will first need to create the model that will store the instances of energy monitoring machines. 
To create the model:
Navigate to Digital Twins. 
The Models pane appears. 
From the Models pane, click the Create a model tile. 
The Create a Model dialog box appears. 
The Create a Model tile
﻿
Click the Type drop-down list and select ASSET.
Enter the name of the model in the Name field (for example, Energy_Monitoring_Model).
(Optional) Enter a short description of the model in the Description field.
Click Add.
The new model appears in the Models pane.
After creating the model, you can configure it﻿ with the following parameters. 
Model Static Attributes
The static attributes will need to be customized for each instance. 
Asset
Line
Model
Manufacturer
Location
Plant
Transformations
The transformations are based on the sample payload above.  
energyRateDescription
JSON
{
  "value": "$energyRateDescription",
  "timestamp": "$timestamp"
}
{
  "value": "$energyRateDescription",
  "timestamp": "$timestamp"
}
﻿
energyRate
JSON
{
  "value": "$energyRate",
  "timestamp": "$timestamp"
}
{
  "value": "$energyRate",
  "timestamp": "$timestamp"
}
﻿
energyUsed
JSON
{
  "value": "$energyUsed",
  "timestamp": "$timestamp"
}
{
  "value": "$energyUsed",
  "timestamp": "$timestamp"
}
﻿
shift
JSON
{
  "value": "$shift",
  "timestamp": "$timestamp"
}
{
  "value": "$shift",
  "timestamp": "$timestamp"
}
﻿
startHour
JSON
{
  "value": "$startHour",
  "timestamp": "$timestamp"
}
{
  "value": "$startHour",
  "timestamp": "$timestamp"
}
﻿
season
JSON
{
  "value": "$season",
  "timestamp": "$timestamp"
}
{
  "value": "$season",
  "timestamp": "$timestamp"
}
﻿
cost
JSON
{
  "value": "$cost",
  "timestamp": "$timestamp"
}
{
  "value": "$cost",
  "timestamp": "$timestamp"
}
﻿
The list of transformations would display as the following. 
List of transformations
﻿
Tip: To access nested metadata keys, you should include a period after the initial dollar sign. For example:
$.metadata will extract the entire metadata object.
$.metadata.$cost will extract the cost key in the metadata object.
Model Dynamic Attributes
The dynamic attributes will use the appropriate schemaID (transformation) to generate values. 
Name
Unit (optional)
DataType (optional)
SchemaID (optional)
cost
USD
﻿
cost
energyRateDescription
﻿
﻿
energyRateDescription
energyRate
USD/kWh
﻿
energyRate
energyUsed
﻿
﻿
energyUsed
shift
﻿
﻿
shift
startHour
﻿
﻿
startHour
season
﻿
﻿
season
power
kW
﻿
﻿
Hierarchy
The output for the model will be based on the hierarchy, which would categorize the dynamic and static attributes into telemetry or properties. It would look like the following. 
﻿
Step 4: Create Instances
Once the model is configured, you will need to Add an Instance﻿ for each energy monitoring device at each site. 
One of the instances is described below based on the topics for the data in the user scenario description. 
Instance Static Attributes
You will customize the model static attributes to the specific instance. 
Attribute Name
Attribute Value
Asset
Machine 2
Line
Line 1
Model
model_112233
Manufacturer
Siemens
Location
Canada
Plant
Toronto
Instance Dynamic Attributes
You will configure the appropriate topics in the user scenario description to the dynamic attributes configured in the model. 
Name
Unit
DataType
Topic
SchemaID (optional)
power
kW
﻿
devicehub.alias.P2_Electrical_Panel.L3_Machine2
﻿
cost
USD
﻿
flowToDT.EMD_L3_Machine2
cost
energyRateDescription
﻿
﻿
flowToDT.EMD_L3_Machine2
energyRateDescription
energyRate
USD/kWh
﻿
flowToDT.EMD_L3_Machine2
energyRate
energyUsed
﻿
﻿
flowToDT.EMD_L3_Machine2
energyUsed
shift
﻿
﻿
flowToDT.EMD_L3_Machine2
shift
startHour
﻿
﻿
flowToDT.EMD_L3_Machine2
startHour
season
﻿
﻿
flowToDT.EMD_L3_Machine2
season
﻿

Name	Unit (optional)	SchemaID (optional)
cost	USD	cost
energyRateDescription		energyRateDescription
energyRate	USD/kWh	energyRate
energyUsed		energyUsed
shift		shift
startHour		startHour
season		season
power	kW

Attribute Name	Attribute Value
Asset	Machine 2
Line	Line 1
Model	model_112233
Manufacturer	Siemens
Location	Canada
Plant	Toronto

Name	Unit	Topic	SchemaID (optional)
power	kW	devicehub.alias.P2_Electrical_Panel.L3_Machine2
cost	USD	flowToDT.EMD_L3_Machine2	cost
energyRateDescription		flowToDT.EMD_L3_Machine2	energyRateDescription
energyRate	USD/kWh	flowToDT.EMD_L3_Machine2	energyRate
energyUsed		flowToDT.EMD_L3_Machine2	energyUsed
shift		flowToDT.EMD_L3_Machine2	shift
startHour		flowToDT.EMD_L3_Machine2	startHour
season		flowToDT.EMD_L3_Machine2	season

Updated 11 Oct 2024

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