The Virtual Sensor API is a software sensor used to serve in place of a physical sensor for variables (called targets) that are too costly or impractical to measure.

It leverages supervised machine learning techniques to learn the relationship between the target variable and a set of heterogeneous available variables.

This API should be used when the target variable is supposed to have an underlying pattern and when historical measurements are available. These measurements could be data collected manually or data collected by a temporary sensor.

This document provides a general tutorial for users who want to consume the Virtual Sensor API.

• The Virtual Sensor API includes 4 features:

• Creates a virtual sensor model based on historical data (createModel endpoint).

• Applies a created model to a test data set (applyModel endpoint).

• Updates an existing model that was built with the createModel endpoint (updateModel endpoint).

• Get information about an existing model (getModelInformation endpoint).

Take the tour and experiment on your own data prior to any development through our hands-on application !

https://try-analytics-se.azurewebsites.net/virtualsensor

The createModel endpoint creates a virtual sensor model. The created model uses information available from driver measurements and process parameters to model the relationship between the drivers and the target.
The principle of this service is the following:

• Historical training data depicting how a system works is available.
• A target variable, i.e., data of special importance for which we want to build a virtual sensor model, is known.
• Some pattern in the data is suspected, i.e. some relationship between some of the available data and the target can be observed.
• This pattern can be used to create a virtual sensor model to generate predictions.

The applyModel web service applies the created model on a input test set in order to predict the value of the target during this period.

The updateModel endpoint updates the model created with the createModel service. The input data should be the same variables as the ones used to create the initial model.

This service returns information on an existing prediction model that is securely stored on the Schneider-Electric platform.

Amount of calls to the API in the SANDBOX are limited.

To get a full experience and extend the thresholds, please subscribe to the API and use the production environment.

Step 1: Setup environment

Step 2: Prepare Data

Step 3: Create a new energy model

Step 4: Train model

Step 5: Apply an existing forecasting model to new data

Step 6: Get model information on an existing prediction model

This API uses API keys to authenticate and allow access to the API.

Your API keys carry many privileges, so be sure to keep them secure!

The API key should be included in all API requests in the Authorization header that looks like the following:

| Authorization: your_subscription_key

For steps on how to generate your_subscription_key please refer to the Features tab.

We follow the error response format proposed in RFC 7807also known as Problem Details for HTTP APIs. As with our normal API responses, your client must be prepared to gracefully handle additional members of the response.

Contact the Exchange support team at exchange.support@se.com.

In your request please :

- mention the involved endpoint

- give the request that generate the error

- copy past any error message you received

- add some screen shot