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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
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.
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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.
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公开(公告)号：US20230259812A1
公开(公告)日：2023-08-17
申请号：US17671092
申请日：2022-02-14
Applicant: Accenture Global Solutions Limited
Inventor： Bhushan Gurmukhdas Jagyasi , Siva Rama Sarma Theerthala , Saurabh Pashine , Soumit Bhowmick , Gopali Raval Contractor
Abstract: This application discloses a system and method for federated collaborative machine learning model development using local training datasets that are not shared. An adaptive and evolutionary approach is used to select local training nodes that are most fit from one training round to the next training round to optimize an overall cost and performance function for the federated learning, to cross-over model architecture between local training nodes, and to perform model architecture mutation within local training nodes. The local training nodes are further clustered to account for the inhomogeneity in the local datasets. Such adaptive, evolutionary, and collaborative federated learning thus provides cost-effective and high-performance model development.
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