Inventus Holdings, LLC

Sriharsha Veeramachaneni

Gary D Moncrief, Jr

Ryan R Butterfield

Matthew D Briercliffe

Jay K Limbasiya

Aaron J G Epel

Mohammadreza Rezaie

Benjamin D Grindy

Kenneth A Williams

Gordon W Paynter

Dennis A Moon

Mario D Ibanez

Lowell Crosby Savage, Iii

James U Morley

Raja D Doake

Richard Mack Argentieri

Kristen T Bradford

A solar tracking system includes a database to store data associated with a solar field comprising physical location parameters of each of a set of solar panels. The physical location parameters can include a relative row-to-row height and spacing of the solar panels. The system also includes a parameter aggregation tool to aggregate the physical location parameters to generate aggregate time-based output power data associated with the solar field based on geolocation data associated with the solar field. The system also includes a solar tracking tool to generate a solar tracking control scheme for the solar field based on the aggregate time-based output power data, the geolocation data associated with the solar field, and a row-to-row backtracking constraint. The set of solar panels of the solar field can implement solar tracking based on the solar tracking scheme.

Solar tracking system for complex terrain

Systems and methods for monitoring an operational system. A data set with output power values and associated environmental data values for an electrical generation system are accumulated. Statistical relationships are determined for output power values and environmental data values. Outlying data is determined based on the statistical relationships and are removed from the data set to create selected data. A regression model is developed from the selected data to map predicted output power values to values of environmental data. Data with present output power values and present associated environmental data for the electrical generation system are later received. Predicted output power values are predicted by the regression model for the present associated environmental data. An output power discrepancy is identified by comparing the predicted output power to the present output power. A notification of an anomaly is provided based on identification of the output power discrepancy.

Adaptive system monitoring using incremental regression model development

One example includes a forecast engine that generates forecast data that characterizes predicted operating conditions of an energy storage system for a given time period in the future, wherein the predicted operating conditions are based on a load history for a power consuming premises coupled to the energy storage system and on a value history for power provided to and consumed from a power grid. The load history of the power consuming premises characterizes unmetered power transferred to the power consuming premises, metered powered transferred from the power grid to the power consuming premises and metered powered exchanged from the energy storage system to the power grid. In the example, a schedule manager generates an operation schedule for operating the energy storage system. The operation schedule includes charge and discharge patterns for an energy storage source that are tuned to curtail power costs and/or elevate power revenue value.

Controlling a behind the meter energy storage and dispatch system to improve power efficiency

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Minneapolis, MN, United States of America

Sriharsha Veeramachaneni