公开(公告)号：US20250023349A1

公开(公告)日：2025-01-16

申请号：US18899042

申请日：2024-09-27

Applicant: ABB S.p.A.

Inventor： Mayukha Pal ,  Barin Karmakar ,  Vineeth Reddy

IPC: H02J3/00 ,  H02J13/00

Abstract: A method for performing asset and energy management for an electrical distribution system includes receiving measurement data of plurality of input variables of electrical distribution system; generating coefficient matrix for each output variable used for asset and energy management, based on effects of plurality of input variables on corresponding output variable, using sparse regression. The coefficient matrix comprises one or more input variables from plurality of input variables. Furthermore, the method comprises determining plurality of system representations for each output variable, based on corresponding coefficient matrix. Each of plurality of system representations indicates relationship between one or more input variables and the corresponding output variable. Thereafter, the method comprises identifying system representation from plurality of system representations to train machine learning model for predicting value of output variable, for performing asset and energy management for electrical distribution system.

公开(公告)号：US20240243575A1

公开(公告)日：2024-07-18

申请号：US18408752

申请日：2024-01-10

Applicant: ABB S.p.A.

Inventor： Mayukha Pal ,  Maneesh Reddy

IPC: H02J3/00

CPC classification number: H02J3/003

Abstract: A method of predicting energy consumption in electrical distribution systems includes receiving time-series data of plurality of input variables and output variable of electrical distribution system from one or more sensors, determining first set of input variables based on effects of plurality of input variables on output variable, by associating time-series data of each of plurality of input variables with time-series data of output variable, determining a second set of input variables and one or more dependency parameters, based on dependency between each variable of first set of input variables and output variable, at past time instances, generating network representation indicating second set of input variables, output variable, and one or more dependency parameters.