ai-toolkit-iot-edge/Energy demand time series

IoT Scenario - Energy Demand Time Series Forecasting

Time series forecasting is the task of predicting future values in a time-ordered sequence of observations. It is a common problem and has applications in many industries. This example focuses on energy demand forecasting where the goal is to predict the future load on an energy grid. Although the context is energy demand forecasting, the methods used can be applied to many other contexts and use cases. For example, package delivery companies need to estimate the demand for their services so they can plan workforce requirements and delivery routes ahead of time. In many cases, the financial risks of inaccurate forecasts can be significant. Therefore, forecasting is often a business critical activity.

This sample shows how time series forecasting can be performed through applying machine learning techniques. You are guided through every step of the modeling process. We have also included a Docker container with the final model. This container can be deployed to an IoT device via Azure IoT Hub.

Link to the Microsoft DOCS site

The detailed documentation for this real world scenario includes the step-by-step walkthrough:

https://docs.microsoft.com/azure/machine-learning/preview/scenario-time-series-forecasting

Link to the Azure Machine Learning Gallery GitHub repository

The public GitHub repository for this real world scenario contains all the code samples:

https://github.com/Azure/MachineLearningSamples-EnergyDemandTimeSeriesForecasting