Solution architecture: Forecast energy and power demand for utilities
Learn how Microsoft Azure can help accurately forecast spikes in demand for energy products and services to give your company a competitive advantage.
This solution is built on the Azure-managed services: Stream Analytics, Event Hubs, Machine Learning Studio, Azure SQL Database, Data Factory and Power BI. These services run in a high-availability environment that is patched and supported, allowing you to focus on your solution instead of the environment they run in.
Deploying to Azure
Use the following pre-built template to deploy this architecture to Azure
Deploying to AzureImplementation guidance
| Products/Description | Documentation | |
|---|---|---|
| Stream Analytics | Stream Analytics aggregates energy consumption data in near real-time to write to Power BI. | |
| Event Hubs | Event Hubs ingests raw energy consumption data and passes it on to Stream Analytics. | |
| Machine Learning Studio | Machine Learning forecasts the energy demand of a particular region given the inputs received. | |
| Azure SQL Database | SQL Database stores the prediction results received from the Azure Machine Learning service. These results are then consumed in the Power BI dashboard. | |
| Data Factory | Data Factory handles orchestration and scheduling of the hourly model retraining. | |
| Power BI | Power BI visualises energy consumption data from Stream Analytics, as well as predicted energy demand from SQL Database. |
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