公开(公告)号：US20220344934A1

公开(公告)日：2022-10-27

申请号：US17242119

申请日：2021-04-27

Applicant: Accenture Global Solutions Limited

Inventor： Jinu Jayan ,  Pallavi S. Gawade ,  Bhushan Gurmukhdas Jagyasi ,  Sandeep Narendra Vaity ,  Pollachi Seetharam Sreedhar ,  Rengaraj Ramasubbu ,  Saurabh Pashine ,  Tamal Bhattacharyya

IPC: H02J3/00 ,  G06Q30/02 ,  G06Q50/06 ,  G06N20/00

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning (ML) to forecast energy demand and to generate an energy plan for one or more facilities of an organization. For example, a system may forecast an occupancy of the facilities for use with historical demand data in forecasting the energy demand. The forecasting may be performed by one or more trained ML models. Additional ML models may be trained to select energy resources that satisfy the forecasted energy demand and that prioritize constraint(s). The system may generate an energy plan that indicates information related to the selected energy resources, such as cost, energy type, environmental impact, etc., for use in increasing an amount of renewable energy resources used at the facilities. In some implementations, the system may recommend actions to reduce a negative environmental impact associated with the selected energy resources.

公开(公告)号：US12184068B2

公开(公告)日：2024-12-31

申请号：US17242119

申请日：2021-04-27

Applicant: Accenture Global Solutions Limited

Inventor： Jinu Jayan ,  Pallavi S. Gawade ,  Bhushan Gurmukhdas Jagyasi ,  Sandeep Narendra Vaity ,  Pollachi Seetharam Sreedhar ,  Rengaraj Ramasubbu ,  Saurabh Pashine ,  Tamal Bhattacharyya

IPC: H02J3/00 ,  G06N20/00 ,  G06Q30/0201 ,  G06Q50/06

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning (ML) to forecast energy demand and to generate an energy plan for one or more facilities of an organization. For example, a system may forecast an occupancy of the facilities for use with historical demand data in forecasting the energy demand. The forecasting may be performed by one or more trained ML models. Additional ML models may be trained to select energy resources that satisfy the forecasted energy demand and that prioritize constraint(s). The system may generate an energy plan that indicates information related to the selected energy resources, such as cost, energy type, environmental impact, etc., for use in increasing an amount of renewable energy resources used at the facilities. In some implementations, the system may recommend actions to reduce a negative environmental impact associated with the selected energy resources.