Widgets Introduction
Page Elements and Data in Dashboards are displayed in the form of Widgets. This section explains how widgets work and how it can be customized to fit your needs.
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Page Elements and Data in Dashboards are displayed in the form of Widgets. This section explains how widgets work and how it can be customized to fit your needs.
Last updated
Widgets are added to the Dashboard using the Dashboard Actions icon on the top right of the screen. Here, you have the option of adding the Widget straight into the Dashboard or into a Grid inside the Dashboard. Refer to the Adding Widgets section to view an example of creating a Dashboard with Widgets.
The function of the Widget does not change when used inside a Grid. The Grid is beneficial for widget placements and organising the position of widgets within the Dashboard. Refer to the Grid and Widget Size section to view further details on Grids and Sizing.
Configuration on all Bitpool Widgets are broken down into a uniform structure.
Title -> Widget Specific Feature -> Data Streams
Users are able to set the default Aggregation and Date Ranges of each widget. These settings are used when Sharing and creating a Presentation of the Dashboard. Here, the user has the ability to enable or disable the use of Global Date Range Setpoint (GDRS) for the particular widget in the Dashboard.
Examples below show common Widget Configuration entry fields and resultant effect on the widget displayed.
Widget Title This is the name displayed for the widget.
Y Axis List Used to show the Units facet for the data (kWh for energy consumption in this example). The user has the ability to enter multiple names when comparing different types of data sets in the same widget (kWh energy vs °C Outside air Temperature etc.).
Configuration of Data Streams See below section for details.
Data Streams follow a uniform structure for all Widgets. Different widgets will contain unique configuration fields. Example below shows a Stream configuration window for the Stacked Chart widget, here, the 'Series Type' and 'Stream Colour' enables the user to change how the data is displayed in the chart. Another example is the ability to choose different colours that correspond with range of values in a Heat Map widget in the Data Visualisation group.
Add Single or Multiple data Streams These buttons enable the user to add a single or multiple data streams into the widget. Multiple Steams button only available in widgets that can display more than 1 data Stream.
Reorder or Delete Reordering arrow buttons will only work when there are multiple data streams. This allows the user to set the order of the data displayed in the widget from first to last. Delete button allows the user to remove the data stream from the widget.
Enable/Disable 'Structured Data' Drop down field for the data allows the user to select the data Stream for this widget. If the data is processed with the Semantics tool, users can see a structured tree by using the toggle button.
Data Label Name for the dataset to be displayed in the widget.
Aggregation Type Ability for users to select the type of data aggregation to be applied. The goal of data aggregation is to condense large datasets to match the user's desired aggregation period when viewing the dashboard. View the Data Aggregation section for more details.
Series Type This button is a widget specific function that allows the user to select how the data is visualised. In this 'Stacked Chart' widget, the user has the ability to chose between multiple type of chart plotting options. Y-Axis This column allows the user to select the Units for the data stream. The drop down menu will only show options the user has entered in the 'Y-Axis List' in the above widget configuration area.
Series Multiplier This field allows users to set a constant multiplier to the data stream. Example: a user can configure a multiplier of 0.001 to convert Watt-hour data to Kilo Watt-hour. Make sure to configure the Y-Axis label to the correct units that should be displayed.
Stream Colour This button allows the user to choose the color of the data stream for this widget. Other widgets may serve different functions.
Numeric data refers to any data that consists of numbers. This can include integers (whole numbers), floating-point numbers (numbers with decimal points). Numeric data is typically used for quantitative measurements or calculations.
Data Received Example: 1, 3.14, -500, 1000
Application:
Temperature readings from different zones: 25.5°C, 21.8°C, 19.3°C, 27.0°C, 22.9°C
Humidity levels in different areas: 45%, 67%, 53%, 59%, 73%
Energy consumption: 523.13 kWh, 759.27 kWh, 948.47 kWh, 1,251.16 kWh, 5,147.93 kWh
Number of cars in Carpark: 29 cars, 194 cars, 231 cars, 305 cars, 482 cars
Kilogrammes of CO2 Emissions per kWh of Energy: 72,573.3 kgCO2e/kWh, 64,923.8 kgCO2e/kWh
Boolean data consists of only two possible values: true or false. It's often used to represent binary conditions. Boolean data is typically represented with either a 1 or a 0.
Data Received Example: 1, 0
Application:
HVAC equipment Status: Running (True/1), Stopped (False/0)
Emergency alarm Status: Activated (True/1), Deactivated (False/0)
Door Status (open/close): Open (True/1), Closed (False/0)
Occupancy Status of Rooms: Occupied (True/1), Unoccupied (False/0)
Equipment Fault Status: Fault (True/1), Normal (False/0)
Enum data, short for "enumerated" data, refers to a type of data that consists of a fixed set of predefined values. Each value in an enum is assigned a symbolic name, making it easier to understand and work with compared to arbitrary numeric or string values. Enums are commonly used to represent a set of related options or categories. Enums do not need to be in numerical order. Empty gaps in values could also be assigned as placeholders for future use.
Data Received Example: 0, 1, 2, 3, 4, 5
Application:
Operational modes of HVAC systems: Off (0), Standby (1), Heating (2), Cooling (3), Ventilation (4)
Alarm severity levels: Low (1), Medium (2), High (3), Critical (4)
Lighting brightness levels: Dim (1), Low (2), Medium (3), High (4), Maximum (5)
Security access levels: Guest (1), Employee (2), Manager (4), Administrator (7)
Lift Floor: Basement 1 (0), Ground Floor (1), Mezzanine (2), Level 1 (3), Level 2 (4)
String data consists of sequences of characters, typically used to represent text or arbitrary data. Strings can include letters, numbers, symbols, and whitespace. String data might be used for user-provided information, such as the name of a scheduled event or the description of a maintenance task.
Data Received Example: "Hello, World!", "123 Main Street", "apple", "12345"
Application:
Names of zones or areas: "Office Floor", "Conference Room", "Lobby", "Server Room", "Restroom
Equipment model numbers: "AHU-1001", "LTG-2002", "FD-3003", "Pump-4004", "Sensor-5005"
Descriptions of alarm conditions: "Fire Detected", "CO2 Levels High", "Water Leak Detected", "Power Failure", "Gas Leak"
User-defined schedules: "Meeting Room Cleaning - Weekly", "HVAC Maintenance - Monthly", "Security Patrol - Daily", "Lighting Inspection - Biweekly".
Instructions for maintenance tasks: "Replace Air Filters", "Check Thermostat Calibration", "Inspect
Ductwork for Leaks", "Flush Plumbing Lines Biannually", "Calibrate CO2 Sensors Semi-Annually"
Cumulative data refers to information that accumulates or adds up over time or across multiple instances. It represents a running total or sum of values. Cumulative data is typically used to track the total amount or quantity of something over a period.
Examples of cumulative data:
Total energy consumption over a month
Cumulative revenue generated by a product over a year
Accumulated rainfall over a season
Examples of cumulative data recorded:
Energy Meter Consumption Data: data is typically a non-resetting counter that only increases
Water Meter Consumption Data: data is typically a non-resetting counter that only increases
Gas Meter Consumption Data: data is typically a non-resetting counter that only increases
Run-time Data: data is typically a non-resetting counter that only increases
Non-cumulative data represents values at specific points in time or instances. Unlike cumulative data, non-cumulative data does not accumulate or add up over time; each data point is independent of the others.
Examples of non-cumulative data include:
Daily temperature readings
Number of occupants in a room at a specific time
Alarm status (Alarm/Normal)
Equipment running status (Running/Stopped)
Examples of non-cumulative data recorded:
Space Temperature reading
Space CO2 reading
Equipment Setpoints
Equipment Airflow reading
Electrical meters power/voltage/current readings
Lighting Lux levels
Door open/closed sensor
Status of equipment (Running/Stopped)
Data aggregation is the process of combining and summarizing individual data points into a single, more manageable dataset. It involves collecting and grouping data based on certain criteria, and then applying a function to calculate a summary statistic or value for each group. The goal of data aggregation is to condense large datasets into smaller, more meaningful summaries that can be easier to analyse and interpret. Data Aggregation is used in Bitpool to group large numbers of raw data into the desired display intervals in the widgets.
Raw Data:
The process starts with raw data, which consists of individual data points in a record. In this example, the raw data are typically recorded in 15 minute intervals.
Grouping Data:
The raw data is grouped into categories or subsets based on specific criteria or dimensions. In Bitpool, the options for grouping the Raw Data is in displaying the data in the widgets. The aggregator will process the Raw Data into 10, 15, 30 minutes, hourly, daily, weekly etc groups.
Applying Aggregation Functions:
Aggregation functions are applied to calculate the data values. Common aggregation functions include sum, average, count, minimum, maximum, etc. These functions are selected by the user in the Data Stream section.
Aggregate Data:
The result of applying aggregation function is a rolled-up version of the data. Here the user will see summarised data to the desired aggregation period with the chosen aggregation method. This is useful when trying to view a large dataset for a long period of time. Example: Viewing hourly records of space temperature through out the week.
Table below shows CO2 data recorded in 15 minute intervals (raw data) aggregated into 1 hour groups. The table shows the mathematical functions of the corresponding aggregation type.
The "Minimum" aggregation type selects the smallest value from the collected dataset within the specified time group. When this aggregator is applied, it scans through the set of raw data points and identifies the lowest value recorded in each group (e.g., every hour, day, or any user-defined period). This is particularly useful for monitoring scenarios when it is critical to identify and analyze the lowest values, such as the minimum temperatures reached in a refrigerated storage area or the lowest CO2 levels in a ventilated room. Using the minimum value can help in understanding the extremes of a dataset and ensures that the lowest recorded data is not lost in the aggregation process.
For example, if CO2 levels are recorded every 15 minutes, applying the Minimum aggregator on an hourly basis would result in a dataset where each hour's value represents the lowest level of CO2 detected during that hour. In the table above, 9:00 AM - 9:45 AM hour has been aggregated to the lowest value of 459 ppm.
The "Maximum" aggregation type is designed to find the largest value within a given set of data points over a specified time frame. When this aggregator is chosen, it sifts through all available raw data and pinpoints the highest recorded value within each aggregate group, such as an hour, day, or another user-defined period. This is especially useful for instances where the highest metric needs to be tracked, like noting the peak temperature of an area, or the maximum occupancy in a space at any given time. With the Maximum aggregation, users can ensure that the highest value is readily visible, which is critical for identifying the upper extremes of a dataset, and making decisions based on those maximum values.
For example, if CO2 levels are recorded every 15 minutes, applying the Maximum aggregator on an hourly basis would result in a dataset where each hour's value represents the highest level of CO2 detected during that hour. In the table above, 9:00 AM to 9:45 AM has been aggregated to the highest value of 519 ppm.
The "Average" aggregation type is utilized to calculate the mean value of a data set within a specific time group. It accounts for all the individual data points by adding them up and then dividing by the total number of points, providing a central value that represents the dataset during the defined time frame. This type of aggregation is particularly useful for analyzing overall trends and patterns over time, such as the average energy consumption in a building, or the average temperature in a room across an hour or a day. By applying the Average aggregator, one can smooth out the fluctuations in data to get a clear understanding of the typical or expected conditions in a given scenario.
For example, if CO2 levels are recorded every 15 minutes, using the Average function on an hourly basis would produce an average of the CO2 concentrations measured within that hour. In the table above, 9:00 AM to 9:45 AM has been aggregated to the average value of 486 ppm.
The "First" aggregation type is employed when it is important to capture the initial value within a dataset for a given time interval. This method selects the very first data point from a sequence within the defined aggregation period, such as the first temperature reading at the beginning of an hour, or the starting occupancy level at the onset of a business day. Utilizing the First aggregator is particularly useful for time-sensitive analyses, where the initial condition or value sets the stage for subsequent events or readings.
For example, if CO2 levels are recorded every 15 minutes, applying the First aggregator on an hourly basis would result in a dataset where each hour's value reflects the very first CO2 measurement taken at the top of that hour. In the table above, 9:00 AM to 9:45 AM has been aggregated to the first value of 459 ppm.
The "Last" aggregation type is an essential tool for capturing the final value in a dataset within a specific time frame. This aggregation method identifies and uses the most recent data point from the sequence of collected data within the allocated grouping period, such as the last recorded temperature at the end of an hour or the occupancy level at the close of a business day. Applying the Last aggregator is particularly useful when the conclusion of a period is of interest, allowing analysts to observe how values culminate at the end of a cycle.
For example, if CO2 levels are recorded every 15 minutes, applying the Last aggregator on an hourly basis would result in a dataset where each hour's value reflects the very last CO2 measurement taken at the bottom of that hour. In the table above, 9:00 AM to 9:45 AM has been aggregated to the last value of 519 ppm, providing key insight into the ending conditions.
The "Difference" aggregation type is designed to calculate the change between the first and last data point within a defined interval. This aggregation can highlight the net increase or decrease of a dataset over a specific time period, such as the total change in CO2 levels across an hour. It's particularly useful for identifying trends like the total consumption of resources or variations in environmental conditions within the aggregated timeframe. By applying the Difference aggregator, users can understand the dynamics of a dataset, whether there's a general upward or downward trend over successive intervals.
For example, with CO2 data recorded every 15 minutes, using the Difference aggregator on an hourly basis could show that from 9:00 AM to 9:45 AM, there was an overall increase of 60 ppm, if the CO2 levels rose from 459 ppm at the beginning to 519 ppm at the end of that hour.
The Difference Aggregator is the best choice to use for Cumulative data sets.
Energy Consumption (kWh) from Electrical Meters
Water Consumption (L/kL) from Water Meters
Gas Consumption (m3) from Gas Meters
Aggregated Data
The chart below shows how the aggregation type selected will affect the visualisation of the data.
