Schneider-Electric Analytics & AI
Published by Schneider Electric


Forecasting analytics makes future prediction based on past and present trends. It uses supervised machine learning techniques to learn the relationship between variables (input) at hand and the variable (the target) we want to forecast.

For instance, this could be the forecast of a production line or the prediction of a daily building energy consumption. Forecasting should be used when the target variable is supposed to have an underlying pattern and when historical measurements are available.

Forecasting analytics includes four features:

- Learn a model from past input and target data (createModel).

- Apply the model on new data to forecast the target (applyModel).

- Update an existing model with actual data (updateModel).

- Get information on an existing model (getModelInformation).


Forecasting Microapp 1

Forecasting Microapp 2