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: 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.