Quick look at District Energy Data Model
By Tom DeWitte and Tom Coolidge
Last month, Esri released an updated version of the District Energy Data Model. This release continues Esri’s practice of maintaining a template data model ready “out-of-the-box” to manage district cooling, district heating, and steam data within an Esri geodatabase.
Why District Energy Data Model
The goal of the Esri District Energy Data Model is to make it easier, quicker, and more cost-effective for district heating, district cooling, and steam utilities to implement the ArcGIS platform. Esri accomplishes this by freely providing a data model that takes full advantage of the capabilities of the geodatabase. The data model is created and tested with ArcGIS products to ensure that it works. This significantly reduces the complexity, time, and cost to implement a spatially enabled district energy data repository.
District Energy Enterprise Data Management
For many district energy utility enterprises, deploying the ArcGIS platform that leverages the concepts of a service-oriented web gis is more than loading the district energy data model into an enterprise geodatabase. It requires additional steps such as creating an ArcGIS Pro map configured for publishing the data model and publishing of the Pro map to create the required map and feature services. To help simplify these additional steps performed with the industry data model, Esri has embedded the data model into a new ArcGIS for district energy solution. The new solution is called District Energy Utility Network Foundation. This solution provides the data model, sample data, and an ArcGIS Pro project configured with tasks and performance optimized maps. You can access this solution from the Esri ArcGIS for District Energy solution site.
A data dictionary for this data model is available online.
Best Practice Use of ArcGIS
This updated version of the District Energy data model is 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 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.
Embeds 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.
Modeling Flow thru your Pipe Network
District energy systems often have a dual set of pipes in a single trench. One flows from the energy facility to the customer, the other flows from the customer back to the energy facility. This makes modeling flow through a district energy pipe system unique among the pipe utilities. This data model supports the dynamic tracing of water flow, heat flow, cathodic electric flow, and leak detection circuits across the dual pipe network.
A Foundation to Build Upon
The official name of this first district energy solution is District Energy Utility Network Foundation. The name was intentional as this spatially-enabled data management solution is the foundation from which many more district energy solutions can be added. With this foundation, district energy organizations can provide analytics, visualization, and data collection solution to their users.
PLEASE NOTE: The postings on this site are our own and don’t necessarily represent Esri’s position, strategies, or opinions.