Opower, Inc.

Richard Tyler Curtis

Erik Shilts

Daniel J Yates

Janet Yu

Ben Packer

Tom Mercer

Eric Chang

Kyle N Yost

Agustin Fonts

Garner Chung

Yoni Ben-Meshulam

Jonathan Z Ben-Meshulam

David Copeland

Illustrative embodiments are directed to methods and computer systems for reporting unusual or anomalous usage a commodity by consumers. A computer system retrieves a set of usage-information datasets corresponding to a set of consumers, each dataset including past usage of the commodity during at least one of a completed billing period and a current usage of the commodity during the current billing period. The computer system establishes a set of report-trigger conditions for the current billing period, each of the report-trigger conditions corresponding to a consumer. The computer system may monitor usage or spending of the set of consumers to determine, for each consumer, whether an estimated usage established for each consumer fulfills the consumer's report-trigger condition. Once the report-trigger condition is fulfilled, the computer system outputs a report to the consumer.

Method and system for unusual usage reporting

Illustrative embodiments of the present invention are directed to methods and computer systems for reporting a consumer's usage of a resource. A computer system retrieves consumer characteristic data and resource usage data for the first consumer and a set of second consumers including characteristic data related to each consumer. The computer system selects at least one consumer that is similar to the first consumer from the set of second consumers based upon a plurality of common criteria between the first consumer's characteristic data and a second consumer's characteristic data. The computer processes may be performing iteratively until the total number of similar consumers is equal to or greater than the predetermined number of consumers. Once a desired number of similar consumers is found, the computer system generates a report that displays the first consumer's resource usage data and the at least one similar consumers' resource usage data.

Method and system for selecting similar consumers

Illustrative embodiments of the present invention are directed to methods and computer systems for reporting unusual or anomalous usage or spending of a commodity by consumers. A computer system retrieves a set of usage-information datasets corresponding to a set of consumers, each dataset including past usage of the commodity during at least one of a completed billing period and a current usage of the commodity during the current billing period. The computer system establishes a set of report-trigger conditions for the current billing period, each of the report-trigger conditions corresponding to a consumer. The computer system monitor usage or spending of the set of consumers to determine, for each consumer, whether an estimated usage established for each consumer fulfills the consumer's report-trigger condition. Once the report-trigger condition is fulfilled, the computer system outputs a report to the consumer.

Techniques for unusual usage alerts without smart meter data are provided. The computing device can determine a set of climate control disaggregation coefficients and obtain weather data corresponding to at least a portion of a billing period. In turn, the computing device can determine first forecasted usage data for the at least a portion of the billing period based on the set of climate control disaggregation coefficients and the weather data, and determine second forecasted usage data for a length of the billing period based at least in part on the first forecasted usage data. In some aspects, the computing device can project unusual energy usage for the billing period based on the second forecasted usage data determined to be greater than usage data for a prior time period. The computing device can generate an alert notification including an indication of the unusual energy usage projection.

Unusual usage alerts

An occupancy schedule determining method and system that receives usage data indicating a quantity of a resource supplied by a utility that is used at the location over a plurality of days, each of the plurality of days being subdivided into a plurality of predetermined time periods, and the usage data indicating the quantity of the resource supplied by the utility that is used during each of the predetermined time periods, aggregates the usage data for each of the plurality of predetermined time periods over the plurality of days, and uses the aggregated usage data to determine the occupancy schedule at the location using a processor.

System and method for determining occupancy schedule for controlling a thermostat

A thermostat set point estimation method and system that selects a plurality of candidate thermostat set points, determines for each of the plurality of candidate thermostat set points a predicted energy usage amount corresponding to the candidate thermostat set point, determines for each of the plurality of candidate thermostat set points an error value corresponding to the candidate thermostat set point using an actual energy usage amount and the predicted energy usage amount corresponding to the candidate thermostat set point, and identifies an estimated thermostat set point by selecting the candidate thermostat set point having the error value that is lowest from the plurality of candidate thermostat set points.

Thermostat set point identification

Methods and computer systems are disclosed for identifying the power-demand of a climate-control system at a premises to reduce usage of energy at the premises. A computer system receives energy usage data and run-time data of the climate-control system. The operational data includes an on-time and/or off-time associated with the climate-control system. The computer system determines a power-draw of the climate-control system using the energy usage data and the run-time data and outputs the power-draw to be used to reduce the energy usage at the premises.

Method to identify heating and cooling system power-demand

The disclosure provides a computer-implemented method and system of reducing commodity usage by providing tailored consumer information to the consumer. The method utilizes neural network and machine learning techniques to calculate and cluster statistical data to classify the premises for desired observable condition, including the presence of a programmed thermostat. A score is determined that corresponds to at least one of: (i) a present state of an observable condition, (ii) a non-present state of the observable condition, and (iii) a degree of a condition of the observable condition, to provide tailored consumer information associated to the consumer's usage of the commodity.

Thermostat classification method and system

Illustrative embodiments of the present invention are directed to a method and system for disaggregating climate control energy use from non-climate control energy use for a building. The method includes receiving a series of building energy use values and corresponding outdoor temperature values for a time period. Each of the energy use values and outdoor temperature values is associated with a time interval. The method further includes determining a series of temperature difference values for the time period based on a difference in temperature between a predetermined baseline temperature and each of the outdoor temperature values. A regression analysis is used to determine a climate control coefficient and a non-climate control coefficient from the energy use values and temperature difference values. The climate control coefficient and/or the non-climate control coefficient is used to determine climate control energy use and/or non-climate control energy use for the building.

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Washington, DC, United States of America

Richard Tyler Curtis