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: Azure Stream Analytics, Event Hubs, Machine Learning Studio, Azure SQL Database and Data Factory. 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.
Implementation guidance
| Products/Description | Documentation | |
|---|---|---|
|
Azure 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 Azure Machine Learning. 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 visualises energy consumption data from Stream Analytics, as well as predicted energy demand from SQL Database. |
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