Hewlett-packard Development Company, L.p.

New York University

Naren Ramakrishnan

Manish Marwah

Bhubaneswar Mishra

Debprakash Patnaik

Marco Antoniotti

Ratnesh Kumar Sharma

Martin Arlitt

Amip J Shah

Naren Sundaravaradan

Prithwish Chakraborty

Systems and automated methods for predicting photovoltaic (PV) generation are provided. Weather forecast data and present-day and historical PV generation data are provided to respective predictors. The predictors derive weighted predictions that are used to calculate a Bayesian model average. Near-future generation by the PV system is predicted using the Bayesian model average. Production rates, worker scheduling, hours of operation and other planning decisions can be made in accordance with the predicted near-future generation.

Predicting near-future photovoltaic generation

Tree discovery systems and methods are disclosed. An example of a method is carried out by program code stored on non-transient computer-readable medium and executed by a processor. The method may include identifying in computer-readable medium a first node for a system under consideration, each node in the computer-readable medium representing a characteristic of a component. The method may include selecting at least a second node from the computer-readable medium for the first node. The method may include discovering in the computer-readable medium at least one system tree for the system under consideration based on the first node and the at least second node.

System and method for tree discovery

In a method for determining operational settings for a plurality of fluid moving devices, one or more motifs in utilization data of the plurality of fluid moving devices collected over a time series is identified as a multivariate time series of data, sustainability metric levels for each of the one or more identified motifs are calculated, and a determination as to which of the one or more identified motifs have favorable sustainability metric levels is made.

Determining operational settings for fluid moving devices using a multivariate time series of data

A method, system and software arrangement in accordance with an exemplary embodiment of the present invention are provided to extract descriptive narrative from numerical experimental data augmented with ontological controlled vocabulary. One exemplary application of such system, method and software arrangement is in organizing gene-expression time course data in terms of biological processes that may be activated and deactivated as the biological system responds to its normal or perturbed environment. The present invention may also have biological applications to drug-or-vaccine discovery, understanding behavior of a cell in an altered diseased state (e.g., cancer, neuro-degeneration or auto-immune disease, etc.), genetically modifying a natural wild-type organism, genetic-engineering, etc. Other exemplary applications may include understanding neural behavior, market behavior of a population of users interacting on the Internet, etc.

Method, system and software arrangement for reconstructing formal descriptive models of processes from functional/modal data using suitable ontology

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Blacksburg, VA, United States of America

Naren Ramakrishnan