District Heating and Cooling Data Model 2020
By Tom DeWitte
Earlier today Esri posted the first ever industry data model for managing District Heating and District Cooling (DHC) pipe systems. This data model continues Esri’s efforts in providing industry specific template data models. The DHC 2020 data model provides District Heating and District Cooling organizations with an implementation ready configuration for Esri Geodatabase data repositories. This data model template is provided as a free download for all to use.
Why Create This Data Model
The goal of the DHC 2020 data model is to make it easier, quicker, and more cost effective for organizations responsible for the management of District Heating or District Cooling pipe systems to implement the ArcGIS platform. The DHC 2020 data model accomplishes this by freely providing a data model template that demonstrates a best practice configuration of the Esri geodatabase to address the system of record needs of DHC organizations.
How was this Data Model Created
Creating a new industry data model requires an immense amount of information. More information than any one person or organization contains. Fortunately, many persons from around the world were willing to share their knowledge and time to help bring this data model to life. This group effort from organizations big and small, public and private, incorporates hundreds of years of industry knowledge into this data model.
Best Practice Application of ArcGIS Technology
DHC 2020 was configured to take advantage of the latest capabilities provided by the ArcGIS Technology. This includes recent enhancements such as contingent values, attribute rules, and the Utility Network. In this data model you will find contingent value configurations to restrict the valid types of pipe insulation and pipe material based on whether the pipe transits, steam, condensate, heated water, or chilled water. Included in DHC 2020 are many attribute rules to automate attribute population as well as provide data quality checks. This data model includes utility network subnetwork configurations for defining pressure zones, circulation areas, leak detection zones, cathodic protection zones, and energy zones. And let’s not forget the thousands of connectivity, containment and association rules of the utility network.
Industry Business Rules
Business rules are a great way to share industry knowledge. Sometimes it is simple, such as ensuring that the maximum operating temperature is greater than the standard operating temperature, or that the in-service date occurred after the date of installation. Others are more complicated, such as knowing when to create a containment association between two assets. Thanks to the combined knowledge of many persons across the industry, these business rule examples and many more are included with this data model.
Sample Data
Included in the download file stored on the District Heating and Cooling industry page of geonet in addition to the data model asset package is a sample dataset. This dataset is fully 3D and provides great examples of the many facilities which participate in a District Heating or District Cooling pipe system.
Data Dictionary
As I noted in my October update, writing a data dictionary is not fun, but it is very important to explaining the what, the how and the why of the data model. Work on the data dictionary is complete and included in the download file as a PDF document.
This data dictionary includes descriptions of each featureclass subtype layer. For each subtype layer a full description of each attribute that is appropriate is provided.
Additionally, the data dictionary includes a listing and description of each attribute rule that is to be included with the data model.
How to Download
This data model, its sample data, and data dictionary are available as a single download from the District Heating and Cooling industry page on geonet. You can download it directly with this link: https://community.esri.com/t5/district-heating-and-cooling/district-heating-and-cooling-data-model-2...
As noted previously, this data model is the cumulation of the input of many persons. This data model can only evolve with your feedback. If you have suggestions for improving this data model or see something that requires correction to help the next person, please reply to this blog and post your feedback, or you can email me directly at: tdewitte@esri.com.