Sap Se

Sangeetha Krishnamoorthy

Sivakumar N

Mario Ponce

James Rapp

Mario Ponce Midence

Pankti Jayesh Kansara

Ninad Kulkarni

Jun Meng

Rui Jin

John Seeburger

Xuening Wu

Wafaa Sabil

The present disclosure involves systems, software, and computer implemented methods for proactively predicting demand based on sparse transaction data. One example method includes receiving a request to predict transaction quantities for a plurality of transaction entities for a future time period. Historical transaction data for the transaction entities is identified for a plurality of categories of transacted items. The plurality of categories are organized using a hierarchy of levels. Multiple levels of the hierarchy are iterated over starting at a lowest level. For each current level in the iteration, features to include in a quantity forecasting model for the current level are identified. The quantity forecasting model is trained using the identified features. Predicted transaction dates are predicted for the current level by a transaction date prediction model. The quantity forecasting model is used to generate predicted quantity information for the current level for the predicted transaction dates.

Proactively predicting transaction quantity based on sparse transaction data

Systems and methods are provided for determining a weather forecast corresponding to a location of an air handling unit for a building, generating a foot traffic forecast for a specified time period in the building, and generating a predicted energy consumption curve based on the weather forecast and generated foot traffic forecast for the specified time period. Based on the predicted energy consumption curve, the systems and methods further provide for generating settings for controllable energy devices of the air handling unit to control the air handling unit for the specified time period.

Predictive air handling system

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Sunnyvale, CA, United States of America

Sangeetha Krishnamoorthy