Rensselaer Polytechnic Institute

International Business Machines Corporation

Koushik Kar

Nitin Gaur

Yada Zhu

Sijia Liu

Lucian Popa

Kumar Bhaskaran

John T Wen

Sandipan Mishra

Santosh K Gupta

Aparna Gupta

Sai Radhakrishna Manikant Sarma Palepu

A graph neural network can be built and trained to predict a risk of an entity. A multi-relational graph network can include a first graph network and a second graph network. The first graph network can include a first set of nodes and a first set of edges connecting some of the nodes in the first set. The second graph network can include a second set of nodes and a second set of edges connecting some of the nodes in the second set. The first set of nodes and the second set of nodes can represent entities, the first set of edges can represent a first relationship between the entities and the second set of edges can represent a second relationship between the entities. A graph convolutional network (GCN) can be structured to incorporate the multi-relational graph network, and trained to predict a risk associated with a given entity.

Multi-relational graph convolutional network (GCN) in risk prediction

A collaborative energy management system, method and program product for a multi-zone space. A system is disclosed including: a plurality of environment sensors located throughout the multi-zone space; an adaptive learning system that collects environment data from the environment sensors and generates a correlation model that correlates historical environment data with HVAC settings; and an optimization system that utilizes the correlation model, inputted preferences received from a plurality of occupants within the multi-zone space, and energy usage goals to periodically generate new HVAC settings for controlling an HVAC system for the multi-zone space.

Collaborative energy management system

Growing community of inventors

Waterford, NY, United States of America

Koushik Kar