Abstract: 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.

Abstract: 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.

Abstract: 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.