US 11,735,920 B2
Cognitive framework for improving responsivity in demand response programs
Rajesh Kumar Saxena, Maharashtra (IN); Harish Bharti, Pune (IN); Anupama Ratha, New Town (IN); and Sandeep Sukhija, Rajasthan (IN)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US)
Filed on Mar. 12, 2021, as Appl. No. 17/199,579.
Prior Publication US 2022/0294221 A1, Sep. 15, 2022
Int. Cl. H02J 3/14 (2006.01); H02J 3/00 (2006.01); G06N 20/00 (2019.01)
CPC H02J 3/144 (2020.01) [G06N 20/00 (2019.01); H02J 3/003 (2020.01)] 20 Claims
OG exemplary drawing
 
1. A computer implemented method comprising:
obtaining, by one or more processors, historical data of demand response programs between one or more provider of a subject energy and a plurality of users and demand response agreements respective to each of the users;
extracting, by the one or more processors, from the historical data and the demand response agreements, attributes relevant to responsivities of the demand response programs;
training, by the one or more processors, a demand-response (DR) user pooling model as a machine learning model with training datasets including the attributes from the extracting and values corresponding to each of the attributes, wherein the DR user pooling model identifies two or more users amongst the plurality of users as a DR user pool, and wherein one or more demands of the demand response programs are responded together by the two or more users in the DR user pool;
predicting, by the one or more processors, a new set of values corresponding to the attributes and the responsivities of the demand response programs as being responded to by the DR user pool; and
adjusting, by the one or more processors, a configuration of the DR user pool according to the new set of values from the predicting, upon ascertaining that an improved responsivities of the demand response programs had been predicted with one or more instances from the new set of values.