Abstract: Systems, methods, and other embodiments associated with a machine learning predictive model for predicting a propensity to implement energy reduction settings are described. Data records including load data for a target group of dwellings is obtained. An empirical load shape is generated for each given target dwelling based on the load data. A target feature vector is generated for each given target dwelling based on at least the empirical load shape corresponding to the given target dwelling. A trained machine learning predictive model is executed on the target feature vectors of the target group of dwellings to identify a set of target dwellings that are likely to reduce electricity consumed in accordance with electricity settings based on at least a generated predicted propensity for a target dwelling to implement the electricity settings.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.