Bidgely, Inc.

Vivek Garud

Abhay Gupta

Hsien-Ting Cheng

Alex Shyr

Ye He

Alaa Kharbouch

Mayank Sharan

Pratik Parekh

Rohit Aggarwal

Subodh Bapat

Rahul Mohan

Uri Rodney

Hsien-Teng Cheng

The present invention teaches methods of performing appliance itemization based on consumption data, including: receiving at a processor the data; determining if the data includes active signals and/or inactive signals; upon detection of an active signal: detecting and estimating active water heating consumption and lighting consumption; upon detection of an inactive signal: detecting and estimating passive water heating consumption, refrigerator consumption; and detecting vacation mode. Methods are disclosed of appliance itemization based whole house consumption data consumption from an advanced metering infrastructure device, the data being at 15, 30, or 60 minute intervals, including: applying disaggregation models to provide detection and estimation of any lighting, water heating, refrigeration, pool pumps, heating, or cooling appliances; applying rule-based models to provide detection and estimation of any cooking, laundry, entertainment, and/or miscellaneous appliances; wherein the disaggregation models and the rule-based models provide for a near complete appliance level itemization and estimation.

Low frequency energy disaggregation techniques

The present invention is generally directed to systems and methods for optimizing energy usage in a household. For example, methods for optimizing energy usage in a household may include steps of: receiving, using an energy optimization device, entire energy profile data associated with the household; obtaining, using the energy optimization device, time of use (TOU) energy pricing structure; processing, the entire energy profile data to generate disaggregated appliance level data related to one or more appliances used in the household; retrieving historical patterns of energy usage of the household during both peak and non-peak time periods; applying a behavior shift analysis on the disaggregated data based at least in part on the TOU energy pricing structure, disaggregated data, and historical patterns of the energy usage; and predicting potential energy savings based at least in part on the behavior shift analysis.

Systems and methods for optimizing energy usage using energy disaggregation data and time of use information

The present invention is generally directed to methods of disaggregating low resolution whole-house energy consumption data. In accordance with some embodiments of the present invention, methods may include steps of: receiving at a processor the low resolution whole house profile; selectively communicating with a first database including non-electrical information; selectively communicating with a second database including training data; and determining by the processor based on the low resolution whole house profile, the non-electrical information and the training data, individual appliance load profiles for one or more appliances.

Energy disaggregation techniques for low resolution whole-house energy consumption data

The present invention is generally directed to systems and methods for performing energy disaggregation of appliances in a home. In accordance with some embodiments of the invention, a method may include receiving one or more parameters corresponding to plurality of the appliances installed in home through an energy disaggregation device. The one or more parameters may be associated with the home. The method may further include receiving localized energy consumption data of a region where the home environment is located, selecting a predefined energy disaggregation model from one or more predefined energy disaggregation models based on the localized energy consumption data, adjusting the predefined energy disaggregation model based on the one or more parameters, and/or applying the adjusted predefined energy disaggregation model to the energy consumption data to perform disaggregation of the energy consumption into a plurality of appliance categories.

Systems and methods for disaggregating appliance loads

The present invention is directed to systems and methods for performing energy disaggregation of a whole-house energy usage waveform, based at least in part on the whole-house energy usage profile, training data, and predetermined generic models, including: a module for pairing impulses identified in the whole-house energy usage waveform to indicate an appliance cycle, pairing impulses with at least one up transition with at least one down transition; a module for bundling impulses that are representative of an appliance cycle; a classification module, which upon determination of a type of appliance associated with bundles, is configured to classify the bundles of transitions in accordance with bundles exhibited by similar appliances with similar characteristics; and utilizing such pairing module and module for bundling to perform energy disaggregation. Moreover, the present invention sets forth graphical user interfaces for the presentation of such data and the receipt of user-supplied validation and information.

Energy disaggregation techniques for whole-house energy consumption data

The present invention is directed to systems and methods of disaggregating and detecting energy usage associated with electric vehicle charging from a whole-house consumption signal. In general, methods of the present invention may include: identifying by an electronic processor potential interval candidates of electric vehicle charging, based at least in part upon long and decreasing patterns; determining by the electronic processor intervals associated with the charging of an electric vehicle, based at least in part on evaluating each potential interval candidate; determining by the electronic processor an initial point of charging for each interval associated with the charging of an electric vehicle; and accounting by the electronic processor for feedback of any incorrectly detected signals.

Electric vehicle disaggregation and detection in whole-house consumption signals

Aspects in accordance with embodiments of the invention may include a method for remotely setting, controlling, or modifying settings on a programmable communicating thermostat (PCT) in order to customize settings to a specific house and user, including steps of: receiving at a remote processor information entered into the PCT by the user; receiving at the remote processor: non-electrical information associated with the specific house or user; and energy usage data of the specific house; performing by the remote processor energy disaggregation on the energy usage data; determining by the remote processor a custom schedule for the PCT based upon the information entered by the user, the non-electrical information associated with the specific house or user, and disaggregated energy usage data; revising by the remote processor, the custom schedule for the PCT based upon additional user input or seasonal changes; providing the custom schedule for the PCT to the PCT.

Applications of non-intrusive load monitoring and solar energy disaggregation

The present invention generally teaches systems and methods for creating appliance signatures based upon whole house composite load profiles. Methods may includes steps such as identifying primitive elements including transients and absolute steady state levels; clustering the primitive elements along multiple dimensions to form impulses; combining impulses to form simple bundles; combining simple bundles with each other or impulses to form complex bundles; and determining specific appliance signatures that substantially match the complex bundles. Methods may also include steps such as determining transitions within the whole house composite load profile; determining household specific appliance state machines for each appliance in the household; and disaggregating the whole house composite load profile into individual appliance energy loads by assigning the determined transitions to the determined household specific appliance state machines.

Growing community of inventors

Cupertino, CA, United States of America

Vivek Garud