Fill Gaps in a iSCAN Channel

Version: 7.2.0

The Prediction Engine uses machine learning to fill (relatively small) gaps in an iSCAN channel and upload the result back to iSCAN.

It will connect to iSCAN, download the data for the provided input_channels and output_channel, use machine learning to reconstruct the output channel, and upload the results to the given prediction_channel.

In order for the prediction to be accurate, there should be a relation between the input columns and the output column, otherwise the machine learning model will not provide an accurate prediction.

For more technical documentation see the Docs

For support you can reach the PI Team at pit@iesve.com

Building URL

e.g. https://iscan.iesve.com/building-details/{Project}/{Building}.

iSCAN Token

You can create a token on the page https://iscan.iesve.com/project-tokens/{Project}.

Input Channels

(optional) Comma-separated list of channels that can influence the channel with gaps that need to be filled.
Can be Names, IDs, or a combination of them.
e.g. Dry Bulb Temperature, Wet Bulb Temperature, SC00001.
Can be left empty if there's no meaningful relationship. In this case only the historical data for the output channel will be used.

Output Channel

Name or ID of the channel with gaps that need to be filled.
e.g. DO10_kitchen||temperature or SC000014

Prediction Channel

Name of the channel where the prediction will be uploaded.
e.g. DO10_kitchen||temperature__prediction

Start Date

Beginning of prediction. The machine learning model will use data previous to this date to learn about the relationships between the input channels and the output channel.
Please make sure that the Output channel has at least 15 days days of good quality data (without gaps) prior to the Start Date.

End Date

End of prediction.

Fill Gaps

Please note that the Prediction Engine can take up to several minutes to successfully run.