Demand Forecasting for Energy

By for March 11, 2016

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====== THIS GALLERY ITEM IS IN MAINTENANCE, WILL BE BACK SOON ======= Accurately forecasting spikes in demand for products and services can give a company a competitive advantage. The better the forecasting, the more they can scale as demand increases, and the less they risk holding onto unneeded inventory. Use cases include predicting demand for a product in a retail/online store, forecasting hospital visits, and anticipating power consumption. This solution template focuses on demand forecasting within the energy sector. Storing energy is not cost-effective, so utilities and power generators need to forecast future power consumption so that they can efficiently balance the supply with the demand. During peak hours, short supply can result in power outages. Conversely, too much supply can result in waste of resources. Advanced demand forecasting techniques detail hourly demand and peak hours for a particular day, allowing an energy provider to optimize the power generation process. This Solution Template using Cortana Intelligence enables energy companies to quickly introduce powerful forecasting technology into their business.
> **Note:** There is a newer version of this solution available [here]( The Cortana Intelligence Suite provides advanced analytics tools through Microsoft Azure — data ingestion, data storage, data processing and advanced analytics components — all of the essential elements for building an energy demand forecasting solution. Use case introduction video: [![Video for Demand Forecasting Solution Template](]( This Solution Template combines several Azure services to provide powerful advantages. Event Hubs collects real-time consumption data. Stream Analytics aggregates the streaming data and makes it available for visualization with PowerBI. HDInsight transforms and aggregates sensor data. Machine Learning implements and executes the forecasting model. The results of the model are stored in Data Warehouse for easy consumption and visualization. Finally, Data Factory orchestrates and schedules the entire data flow. Try it today! > **Note**: In order to deploy the solution, the user must be logged onto Azure Services. Sign in before you click 'Deploy'. If you have already deployed the solution template, click [LAUNCH]( to view. ## Next Steps * Step 1. **Download the sample data generator application after** successful deployment to start sending simulated data to the Event Hub. Follow the [instructions]( in the ReadMe file to **start the data generator** on your machine. * Step 2. **Monitor if data is flowing into your pipeline**, by following the instructions in the [Technical guide]( * Step 3. Read [Technical guide]( for more information. * Step 4. Lastly, **build your Power BI dashboard** using the instructions from the [Technical guide]( ## Pricing Info Your Azure subscription used for the deployment will incur consumption charges on the services used in this solution. For pricing details, visit the [Azure Pricing Page]( See the ‘Services Used’ section on the right pane for the list of the services used in this solution. > Please ensure you **stop the data generator** when not actively using the solution. Running the data generator (Simulated Data Source) will incur higher costs. > > **Please delete the solution if you are not using it**. ## Additional Reference * [Playbook]( A reference for demand forecasting solutions with emphasis on major use cases. * [Architecture diagram]( The diagram provides an architectural overview of the Cortana Intelligence Solution Template for predictive demand forecasting. * [Technical guide]( Detail explaination of the reference architecture and different components that will be provisioned in your subscription as part of this Solution Template. ## Prerequisite * If you don't have an Azure subscription, get started with [Azure free subscription]( * You also need to download [Power BI desktop](