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: Aspects of the subject technology relate to a system that analyzes customers' AMI load curves, identifies evening peak users as defined by their load curves, and provides Energy Efficiency (EE) advice related to their periods of high use. For example, identified high evening users can be sent an email with normative comparisons on evening load use, along with tips to reduce energy usage. Other aspects relate to the additional targets/communications. Aspects of the subject technology relate to categorizing a user's energy consumption tendencies based on a user's load curve and providing customized content based on the user's category. By taking into consideration the user's actual energy consumption patterns, the system may be able to provide more relevant content to the user.

Abstract: The subject disclosure relates to systems and methods for calculating a thermal sensitivity score. In some aspects, the method includes steps for receiving temperature information for a first geographic location, receiving resource consumption data for each of a plurality of similar consumption locations in the first geographic location, and generating, by the processors, a thermal sensitivity model based on the temperature information and the resource consumption data for each of the plurality of similar consumption locations. In some aspects, the steps can further include computing a thermal sensitivity score for each of the similar consumption locations using the thermal sensitivity model. Systems and machine readable media are also provided.

Abstract: Aspects of the subject technology relate to a system that analyzes customers' AMI load curves, identifies evening peak users as defined by their load curves, and provides Energy Efficiency (EE) advice related to their periods of high use. For example, identified high evening users can be sent an email with normative comparisons on evening load use, along with tips to reduce energy usage. Other aspects relate to the additional targets/communications. Aspects of the subject technology relate to categorizing a user's energy consumption tendencies based on a user's load curve and providing customized content based on the user's category. By taking into consideration the user's actual energy consumption patterns, the system may be able to provide more relevant content to the user.

Abstract: Aspects of the subject technology relate to a system that analyzes customers' AMI load curves, identifies evening peak users as defined by their load curves, and provides Energy Efficiency (EE) advice related to their periods of high use. For example, identified high evening users can be sent an email with normative comparisons on evening load use, along with tips to reduce energy usage. Other aspects relate to additional targets/communications. Aspects of the subject technology relate to categorizing a user's energy consumption tendencies based on a user's load curve and providing customized content based on the user's category. By taking into consideration the user's actual energy consumption patterns, the system may be able to provide more relevant content to the user.

Abstract: A residential home characteristics inferring method and system that receives information about energy usage by an energy user, determines using a processor and the received information about energy usage average daily usage during a heating season and average daily usage during a shoulder season, and identifies the fuel type used for heating by the energy user using the determined average daily usage during the heating season and the determined average daily usage during the shoulder season.

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: The subject disclosure relate to a method for calculating a wind sensitivity score. In some aspects, a method of implementing the technology can include steps for receiving wind speed information for a first geographic location over a first time period, receiving resource consumption data for each of a plurality of similar consumption locations in the first geographic location over the first time period; and generating a wind sensitivity model based on the wind speed information and the resource consumption data for each of the plurality of similar consumption locations. In some aspects, the method may further include steps for computing a wind sensitivity score for each of the similar consumption locations using the wind sensitivity model.

Abstract: Aspects of the subject technology relate to methods and systems for classifying businesses according to an open-hours classification category, for example, based on resource consumption data. In some implementations, methods of the subject technology include steps for receiving a plurality of operating hours categories, wherein each of the operating hours categories is associated with at least one usage profile, and receiving business information for an unclassified business, wherein the business information comprises resource consumption data for the unclassified business. In some aspects, methods of the subject technology can further include steps for determining if the unclassified business should be associated with one of the operating hours categories based on the resource consumption data for the unclassified business. Systems and machine-readable media are also provided.

Abstract: Aspects of the subject technology relate to methods and systems for classifying businesses based on various types of information, such as resource consumption information. In some implementations, methods of the subject technology include steps for receiving a plurality of business categories, wherein each of the business categories is associated with at least one category profile, and receiving business information for an unclassified business, wherein the business information comprises behavioral attribute information corresponding with the unclassified business. In some implementations, the methods disclosed herein can further include steps for comparing the business information to one or more of the category profiles to determine if the unclassified business should be associated with at least one of the plurality of business categories.

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 method and system for configuring a resource conservation program that receives information about a level of responsiveness for each of a plurality of users, receives information about resource usage for each of the plurality of users, determines an expected value, using a processor, for each of the plurality of users, using the received information about the level of responsiveness and the received information about resource usage, and configures the resource conservation program based on the determined expected value for each of the plurality of users.

Abstract: The subject disclosure relates to systems and methods for calculating a thermal sensitivity score. In some aspects, the method includes steps for receiving temperature information for a first geographic location, receiving resource consumption data for each of a plurality of similar consumption locations in the first geographic location, and generating, by the processors, a thermal sensitivity model based on the temperature information and the resource consumption data for each of the plurality of similar consumption locations. In some aspects, the steps can further include computing a thermal sensitivity score for each of the similar consumption locations using the thermal sensitivity model. Systems and machine readable media are also provided.

Abstract: The subject disclosure relate to a method for calculating a wind sensitivity score. In some aspects, a method of implementing the technology can include steps for receiving wind speed information for a first geographic location over a first time period, receiving resource consumption data for each of a plurality of similar consumption locations in the first geographic location over the first time period; and generating a wind sensitivity model based on the wind speed information and the resource consumption data for each of the plurality of similar consumption locations. In some aspects, the method may further include steps for computing a wind sensitivity score for each of the similar consumption locations using the wind sensitivity model.

Abstract: This disclosure relates to systems and methods for calculating a solar sensitivity score. In some implementations, a method of the subject technology may include steps for receiving solar exposure information for a first geographic location over a first time period, receiving resource consumption data for each of a plurality of similar consumption locations in the first geographic location over the first time period, and generating a solar sensitivity model based on the solar exposure information and the resource consumption data for each of the plurality of similar consumption locations. In some aspects, the method may further include steps for computing a solar sensitivity score using the solar sensitivity model.

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.

Abstract: The disclosure provides a computer-implemented method and system of reducing commodity usage by providing tailored consumer information to the consumer. The method utilizes neural network and machine learning techniques to calculate and cluster statistical data to classify the premises for desired observable condition, including the presence of a programmed thermostat. A score is determined that corresponds to at least one of: (i) a present state of an observable condition, (ii) a non-present state of the observable condition, and (iii) a degree of a condition of the observable condition, to provide tailored consumer information associated to the consumer's usage of the commodity.

Abstract: Aspects of the subject technology relate to methods and systems for classifying businesses according to an open-hours classification category, for example, based on resource consumption data. In some implementations, methods of the subject technology include steps for receiving a plurality of operating hours categories, wherein each of the operating hours categories is associated with at least one usage profile, and receiving business information for an unclassified business, wherein the business information comprises resource consumption data for the unclassified business. In some aspects, methods of the subject technology can further include steps for determining if the unclassified business should be associated with one of the operating hours categories based on the resource consumption data for the unclassified business. Systems and machine-readable media are also provided.

Abstract: Aspects of the subject technology relate to methods and systems for classifying businesses based on various types of information, such as resource consumption information. In some implementations, methods of the subject technology include steps for receiving a plurality of business categories, wherein each of the business categories is associated with at least one category profile, and receiving business information for an unclassified business, wherein the business information comprises behavioral attribute information corresponding with the unclassified business. In some implementations, the methods disclosed herein can further include steps for comparing the business information to one or more of the category profiles to determine if the unclassified business should be associated with at least one of the plurality of business categories.