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, SQL Database, Data Factory and Power BI. These services run in a high-availability environment, patched and supported, allowing you to focus on your solution instead of the environment they run in.

Deploy to Azure

Use the following pre-built template to deploy this architecture to Azure

Deploy to Azure
Azure Data Factory Energy Demand Forecast(SQL) Energy Demand Forecast(Machine Learning) Geography Data(Blob Storage) Power BI Sample Data Raw event data queue(Event Hubs) Stream Analysis and Data Movement(Stream Analytics)

Implementation guidance

Products 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.

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 visualizes energy consumption data from Stream Analytics as well as predicted energy demand from SQL Database.

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