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jmytheen
Microsoft Employee
Joined 1 year ago
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Power Distribution Outage Management Ontology
Ontology Link The Power Distribution Outage Management Ontology represents the operational knowledge graph for an electric utility's distribution system. It models the complete lifecycle of power delivery—from high-voltage substations stepping down electricity through feeders and distribution transformers to individual customer service points—alongside the crews, work orders, and outage events that maintain system reliability. This ontology captures not just physical infrastructure, but the operational context needed for intelligent decision-making during both routine maintenance and emergency response scenarios. The entities and relationships were chosen to mirror how distribution operators actually reason about the grid: understanding that an outage on Feeder doesn't just affect anonymous customers, but impacts specific critical facilities (hospitals, schools, fire stations) with strict restoration SLAs, and requires dispatching crews with the right skills based on proximity and workload. By encoding these relationships—such as which transformers serve critical customers, which crews are based near which substations, and which work orders affect specific feeders—the ontology enables AI agents to answer complex questions like "Which crew should I assign to restore power to the hospital on Feeder F-12?" without requiring the agent to understand utility operations from scratch. This semantic layer transforms raw data into actionable operational intelligence.Fabric Notebook for Power BI: Deploying Semantic Models and Reports via REST APIs
Overview This solution provides a streamlined approach to deploying Power BI Semantic Models and Reports from Development to Test and Production environments. It is designed to work seamlessly across: Same or different tenants Same or different subscriptions Key Highlights Cross-Environment Compatibility: Works across tenants and subscriptions without additional complexity. Notebook-Driven Automation: Built for Fabric Notebooks, leveraging Python/PySpark for flexibility Reusable & Scalable: Adaptable for multiple workspaces and semantic models How It Works The following steps apply to a same-tenant scenario. For a cross-tenant setup—where the source workspace belongs to one tenant and the target workspace to another—you should first run the semantic model definition and report definition extraction in the source tenant workspace. Then, save the resulting definition JSON to a Lakehouse path in the target workspace. Finally, execute the deployment notebook from the target tenant, using the saved path to deploy both the semantic model and its associated report. Steps 1) Required Inputs Semantic Model ID & Name (Source Environement) Report Id (Source Environement) Workspace ID & Name (Target Environment) 2) Execute the Notebook for Semantic Model Get definition Get the Definition of Existing Dev Semantic Model by Using Get Definition API Add the Model Definition Name and Descriptions in the definition json Save the Model Definition json in a path (optional) 3) Execute the Notebook for Semantic Model Deployment Use Create Semantic Model API With Model definition as request payload Use Long Running Operation APIs to get the status and result Get the Newly created Semantic Model Id for higher environment (Test or Prod) 4) Execute the Notebook for Report Get definition Get the Definition of Existing Dev Power BI Report by Using Get Definition API Add the Report Name and Descriptions in the definition json Save the Report Definition json in a path (optional) 5) Execute the Notebook for Report Deployment Use Create Report API With updated definition as request payload Use Long Running Operation APIs to get the status and result The Semantic Model and Report are now available in the Test or Production workspace. Full Notebook Code : Refer Here https%3A%2F%2Fgithub.com%2Fjmytheen%2FJasmin_Work%2Fblob%2Fmain%2FDeploy_Semantic_Model_PowerBI_Report_Using_FabricRESTAPI.ipynb
