Facilities Management (FM) services are an integral element of modern buildings and real estate infrastructures. FM services play a key role in ensuring proper operating conditions for any building towards safeguarding comfort, safety and efficiency.
They deal with a broad range of business processes including building and asset maintenance (e.g. assets’ field service and capacity planning), project management and human resources (e.g. tracking and allocating tasks), as well as emergency management (e.g. ensuring the safety of operations).
For instance, the integration of sensors and sensor networks within FM processes enables the automated identification of dangerous situations, while at the same time facilitating the discovery and tracking of energy usage patterns. Through such patterns, facilities managers can optimize operating costs and offer the best possible comfort to tenants and employees.
In this context, facilities management processes can also benefit from IoT-enabled visualizations of the status of the infrastructures. An example of these visualizations are Room Temperature Heatmaps.
Read on to find out:
- What Are Heatmaps?
- How to Use Heatmaps in Facilities Management
- How to Develop a Heatmap
- Four Tools to Simplify the Development of Heatmaps
What Are Heatmaps?
Heatmaps provide graphical representations of data by using a range of colors to represent the density of various parameters. They are among the most common visualizations in data science and analytics.
A popular application is in marketing and advertising, where heatmaps are used to visualize users’ behavior, especially when users interact with web pages (e.g., landing pages or web pages templates). Heatmaps visualize user behavior when using web pages which allows marketers to track different user interaction metrics. These metrics include click tracking, scrolling, mouse tracking, and eye-tracking statistics.
Heatmaps are also used in IoT applications. IoT heatmaps display metrics collected by IoT devices that measure physical quantities like temperature, humidity, light, energy consumption and human activity. Such heatmaps can become very powerful business intelligence tools for many different industrial applications, including facilities management.
How to Use Heatmaps in Facilities Management
Facilities Management heatmaps display information visualized by various sensors such as temperature, energy monitoring and human activity sensors.
In many cases, relevant static or semi-static information can be superimposed over FM cyber-representations to provide powerful visualizations that are used to boost process improvement and optimize FM-related decisions.
Some of the most common uses of heatmaps in facilities management applications are:
- Identifying and Tracking Peaks of Usage: A heatmap is a good tool for identifying the peak usage of rooms and their HVAC (Heat Ventilation and Air Conditioning) devices. Other parameters that can be tracked include room occupancy, room temperature and energy consumption.
- Comparing Conditions of Different Rooms or Departments: Simultaneous displaying the temperature conditions of different rooms or segments of the building can provide insights on the effectiveness of facilities management services across rooms, departments or tenants.
- Fine-tuning and Optimization of Schedules: Heatmaps can be useful for facilities managers that seek to optimize usage schedules for various building assets. FM managers can consult heatmaps to identify how existing schedules affect occupancy and to initiate relevant remedial actions.
- Discovery of Uses that Overload Certain Assets: The continuous tracking of sensor values in certain rooms or segments of a building can also facilitate the discovery of use cases that lead to abnormal conditions for various assets, improving building operations.
How to Develop a Heatmap
From a technical perspective, the development of a heatmap is a straightforward process. It is similar to most data pipelining processes and entails the following steps:
- Sensor Deployment: As a first step, the sensor data sources that provide the data of the heatmap must be deployed. To ensure that the heatmap application is robust and sustainable, there is a need for sensors that provide credible values and have long battery life.
- Data Acquisition and Ingestion: Sensor data should be streamed to appropriate data storage infrastructures or some form of cloud storage. Proper connectors to the sensors have to be developed, along with the required data filtering functions.
- Data Pre-processing: In several cases, sensor data must undergo additional transformations before feeding analytics functions. Such transformations include the parsing of the sensor data and their conversion to the input formats required by data analytics.
- Data Analytics: This step involves geometrical and statistical computations towards deriving the values that will drive the display of the different colors of the heatmap. In some cases, heatmaps can benefit from the extraction of usage or behavioral patterns from historical data using more advanced machine learning algorithms.
- Visualization: The final step lays out the computed values on a proper dashboard. As already outlined, other values or graphics may be superimposed over the heatmap visualization to improve its functionality.
As always, the devil is in the details: Small mistakes in any of the steps can make things go wrong.
For instance, sensor deployment is always challenging as it must ensure that the collected data are credible i.e. resilient to sources of interference that can lead to wrong values and outliers.
Moreover, the placement of the sensors affects the calculations of the heatmap values, as the room or building layout is a critical parameter for deriving heatmap values. Likewise, the data analytics step may involve the comparative testing and evaluation of alternative algorithms, which asks for flexibility in refactoring and redeploying the pipeline of the heatmap.
Four DT Tools To Simplify the Development of Heatmaps
The Disruptive Technologies (DT) ecosystem offers access to all the technical ingredients of an effective heatmap oriented solution.
- DT has a broad range of reliable wireless sensors that feature long battery life and can be effectively integrated with cloud computing infrastructures thanks to properly designed Cloud Connectors.
- DT’s solutions are fully programmable and configurable via APIs (Application Programming Interfaces). APIs make it very easy for developers to write applications that harness data from one or more DT sensor products.
- Application development in the DT ecosystem is greatly facilitated by the DT Studio, a complete, integrated programming environment for DT sensors and their Cloud Connectors. Using the DT Studio application, developers can leverage the functionalities of DT products in a visual, developer-friendly way, including drag-and-drop features. Most importantly, DT Studio facilitates the implementation of sophisticated programming logic based on mainstream programming platforms like Python.
- The DT Ecosystem offers a rich knowledge base of support articles, examples and application notes that provide developers with instant answers to most of their concerns and questions.
Implementing a heatmap in the Disruptive Technologies ecosystem can be fast and effective. The above-listed tools elevate the developers’ productivity and enable them to focus on implementing business logic rather than spending time on interfacing with sensors and streaming their data in the cloud.