摘要:
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.
摘要:
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: a method of electronically detecting and disaggregating a consumption signal associated with the charging of an electric vehicle from a whole-house profile, comprising: identifying by an electronic processor potential interval candidates of electric vehicle charging; 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 against factors including amplitude, duration, and time-of-day; and accounting by the electronic processor for feedback of any incorrectly detected signals.
摘要:
The invention is directed to systems and methods for detecting occurrence of at least one event in a household environment. In one embodiment, the method includes receiving energy profile data determined for the household environment. The method may further include analyzing the energy profile data to obtain operation parameters for appliances being used in the household environment. The method may further include comparing the operation parameters with steady state parameters for each of the appliance based one or more deviation rules. The method may further include detecting occurrence of the at least one event in the household environment based on the comparing.
摘要:
Systems and methods of the present invention are directed to disaggregating the contribution of solar panels from a whole house energy profile. Methods of disaggregating energy produced by solar panels from low frequency whole-house energy consumption data for a specific house, may include steps of: predicting solar energy generation for the specific house by estimating a solar capacity of the solar panels, predicting solar intensity associated with the specific house, and multiplying estimated solar capacity with predicted solar intensity; and subtracting the predicted solar energy generation from the low frequency whole house energy consumption data, thereby disaggregating the contribution of energy produced by the solar panels. Computerized systems of the same may apply machine learning models such as radial basis function, support vector, or neural network machines.
摘要:
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.
摘要:
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.
摘要:
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.
摘要:
The present invention is generally directed to systems and methods for managing energy usage in a household. Exemplary methods may include receiving, using an energy management device, entire energy profile data associated with the household generated in a first time period; disaggregating, using the energy management device, the entire energy profile data to determine energy usage associated with one or more appliances used in the household; retrieving, using the energy management device, energy usage of the household generated in a second time period; detecting, using the energy management device, one or more deviations in the disaggregated energy data generated in the first time period based on the energy data of the household generated in the second time period; and identifying, using the energy management device, one or more causes of the one or more deviations.
摘要:
The present invention is generally directed to systems and methods for learning appliance signatures based at least in part upon, energy disaggregation techniques and user input Methods of the present invention may include retrieving energy consumption data pertaining to at least one home environment comprising one or more appliances; identifying one or more patterns in the energy consumption data by applying signal processing algorithms to the consumption data; generating at least one question for a user based at least in part on the one or more patterns; receiving a user input In response to the question; determining at least one appliance in the home environment, based at least in part on the one or more patterns and the user input; and determining an appliance signature by extracting a canonical pattern from the energy consumption data based at least in part on the user input.
摘要:
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.