Sap Se

Ninad Kulkarni

Jing Wang

Sivakumar N

Pankti Jayesh Kansara

Mario Ponce Midence

James Rapp

Mario Ponce

Jun Meng

Sangeetha Krishnamoorthy

Xuening Wu

Rui Jin

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 dates 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, a plurality of transaction date prediction models are trained and tested. Heuristics for the plurality of trained transaction date prediction models are compared to determine a most accurate transaction date prediction model. The most accurate transaction date prediction model is used to make a prediction of transaction dates for the current level for the future time period.

Proactively predicting transaction dates 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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Dublin, CA, United States of America

Ninad Kulkarni