Oracle International Corporation

Oren Benjamin

Selim Mimaroglu

Anqi Shen

Arhan Gunel

Erik Shilts

Ben Packer

Justine Kunz

Ziran Feng

Embodiments implement non-intrusive load monitoring using a novel learning scheme. A trained machine learning model configured to disaggregate device energy usage from household energy usage can be stored, where the machine learning model is trained to predict energy usage for a target device from household energy usage. Household energy usage over a period of time can be received, where the household energy usage includes energy consumed by the target device and energy consumed by a plurality of other devices. Using the trained machine learning model, energy usage for the target device over the period of time can be predicted based on the received household energy usage.

Non-intrusive load monitoring using machine learning and processed training data

Embodiments generate machine learning predictions to discover target device energy usage. One or more trained machine learning models configured to discover target device energy usage from source location energy usage can be stored. Multiple instances of source location energy usage over a period of time can be received for a given source location. Using the trained machine learning model, multiple discovery predictions for the received instances of source location energy usage can be generated, the discovery predictions comprising a prediction about a presence of target device energy usage within the instances of source location energy usage. And based on the multiple discovery predictions, an overall prediction about a presence of target device energy usage within the given source location's energy usage over the period of time can be generated.

Trained models for discovering target device presence

Embodiments implement non-intrusive load monitoring using machine learning. A trained convolutional neural network (CNN) can be stored, where the CNN includes a plurality of layers, and the CNN is trained to predict disaggregated target device energy usage data from within source location energy usage data based on training data including labeled energy usage data from a plurality of source locations. Input data can be received including energy usage data at a source location over a period of time. Disaggregated target device energy usage can be predicted, using the trained CNN, based on the input data.

Non-intrusive load monitoring using machine learning

Embodiments implement non-intrusive load monitoring using ensemble machine learning techniques. A first trained machine learning model configured to disaggregate target device energy usage from source location energy usage and a second trained machine learning model configured to detect device energy usage from source location energy usage can be stored, where the first trained machine learning model is trained to predict an amount of energy usage for the target device and the second trained machine learning model is trained to predict when a target device has used energy. Source location energy usage over a period of time can be received, where the source location energy usage includes energy consumed by the target device. An amount of disaggregated target device energy usage over the period of time can be predicted, using the first and second trained machine learning models, based on the received source location energy usage.

Non-intrusive load monitoring using ensemble machine learning techniques

Systems, methods, and other embodiments associated with predicting energy efficiency program participation are described. Embodiments of a method include obtaining load data for an energy customer, and determining an empirical load shape for the energy customer based on the obtained load data. A defined load shape that most closely matches the empirical load shape is selected for the energy customer. A data structure is generated to include the defined load shape, demographic data and/or site parcel data. It is determined that the energy customer is more likely to participate in an energy efficiency program than a different energy customer by applying a trained predictive model to the data structure. Transmission of information about the energy efficiency program is controlled by assigning a higher priority to a transmission to the energy customer than to a second transmission to the different energy customer.

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Arlington, VA, United States of America

Oren Benjamin