question

How might utilities use the information and data received from the smart meters to specifically target ecologically minded consumers (B2C) and business (B2B) customers?

information

Patent Application Titled "Customer Energy Consumption Segmentation Using Time-Series Data" Published Online

Marketing Weekly News

2015 JUL 4 (VerticalNews) -- By a News Reporter-Staff News Editor at Marketing Weekly News -- According to news reporting originating from Washington, D.C., by VerticalNews journalists, a patent application by the inventors Flora, June (Menlo Park, CA); Kwac, Jungsuk (Stanford, CA); Rajagopal, Ram (Palo Alto, CA), filed on December 11, 2014, was made available online on June 18, 2015.

No assignee for this patent application has been made.

Reporters obtained the following quote from the background information supplied by the inventors: "After utilities deploy large numbers of advanced metering infrastructure meters, i.e., smart meters, across their distribution grids, they are challenged with managing a massive set of 1-hour or 15-minute interval energy consumption data and decoding the information into meaningful measures that can be helpful to them. Also, with the emerging smart grid technologies becoming ubiquitous, utilities must expand their focus from service reliability to service marketability. Because customers vary widely in their usage, needs, and suitability for different programs and pricing packages, this is a challenging, unsolved problem in the industry."

In addition to obtaining background information on this patent application, VerticalNews editors also obtained the inventors' summary information for this patent application: "Existing approaches to analyzing utility customer data rely on demographic variables to segment consumers and target them without high resolution consumption data. The approach of the present invention avoids this problem by incorporating time-series consumption data into customer segmentation by appropriate feature (metric) extraction for a given purpose.

"The smart meter data provides a unique opportunity to understand a customer's energy use for any data-driven energy management plan. Defining and describing different customer segments will provide decision makers with information to advance not only in pricing and program marketing, but also resource allocation and program development. More intimate modeling and analysis of customer behavior can aid utilities in planning ahead instead of reacting to what has already occurred. Among many key applications, customer lifestyle segmentation can unlock potential energy savings and can help utilities understand operating requirements and better coordinate energy resources for grid management.

"In one aspect, the invention provides a method to segment customers' lifestyles based on their utility resource consumption data using the pre-processed load-shape dictionary. Hourly data gathered from residential smart meters is used to empirically define customer segments that can be approached for achieving higher returns in energy programs, such as demand response (DR). The segmentation method uses an encoding system with a pre-processed load shape dictionary that is used to classify customers according to extracted features such as entropy of shape code which measures the amount of variability in consumption. Load shape information enhances our ability to understand individual as well as groups of consumers. For example, time of day building occupancy and energy consuming activities can be interpreted from these shapes.

"Significant features of embodiments of the invention include the full data-driven approach, including a segmentation that can be directly used for energy program targeting, various metrics themselves which can be used for improve targeting performance, and the scalable segmentation process that can work well even on huge amount of data.

"In one aspect, the invention provides a methodology that utilizes energy consumption (electricity, gas or water) data from individual utility consumers to segment the customers based on various features (e.g., lifestyle features). The methodology may include, as appropriate, (1) customer energy consumption profile dictionary generation, (2) customer (energy consumption) lifestyle segmentation, and/or (3) various energy consumption feature (or metric) extraction processes. The method has applications to segmenting the customers based on their lifestyle features and can be used to enhance targeting recruitment in utility programs (demand response, energy efficiency) by utilizing proper energy consumption features (or metrics).

"Embodiments of the invention decompose the daily usage patterns into daily total usage and a normalized daily load shape. Representative load shapes are found utilizing clustering algorithms (in particular, adaptive K-means) and summarized utilizing hierarchical clustering, so a stable encoding mechanism can be designed. Various features and metrics can be extracted from the encoded data by the encoding system provided by embodiments of the invention.

"Embodiments of the invention provide several different segmentation schemes that can be selected for certain program development, pricing, and marketing purposes, e.g., there are five segmentation analyses in one of the papers attached. The invention also significantly provides how to do customer energy consumption lifestyle segmentation with a scalable approach.

"Many features can be extracted from load shapes. In DR programs, peak usage fraction, peak time and peak duration can be important features to better control the demand at peak time. For EE programs, important information are features which can be used as proxy variables of the existence of specific appliances and their efficiency. For example, load sensitivity to temperature during summer can be a proxy variable of air conditioner existence. Besides, many other features can be extracted from this raw usage data depending on the interests of possible programs.

"According to one aspect, the invention provides a method implemented by a computer for segmenting utility customers according to consumption lifestyle features. The method includes collecting by the computer from smart meter sensors time-series utility consumption data from individual utility customers; standardizing by the computer the collected time-series utility consumption data by dividing the time-series data into daily consumption profiles; generating by the computer a utility customer consumption profile dictionary from the standardized data, where the dictionary comprises representative load shapes found using clustering; encoding by the computer the standardized data, wherein the encoding comprises producing a series of dictionary codes using a distance metric and the dictionary of representative load shapes; extracting by the computer consumption lifestyle features of the utility customers from the encoded data; and segmenting by the computer the customers based on the extracted features by clustering (e.g., adaptive K-means clustering, which may using distance metric such as cosine between feature lifestyle vectors).

"The time-series utility consumption data preferably represent resource use per unit time for each customer. The representative load shapes in the dictionary may be found using adaptive K-means and hierarchical clustering. Each of the lifestyle features of the utility customers is preferably a dictionary code distribution vector for each customer. The segmenting of the customers may include adaptive K-means clustering using a distance metric to measure the distance between feature lifestyle vectors. In some embodiments, the segmentations of customers may be used to estimate customer performance in a utility program. The method may also include presenting to customers information about their typical patterns of consumption and savings. The method additionally may include designing pricing of the utility resource based on the encoded patterns, and/or targeting customers with utility programs based on the segmentations. A load shape predictor may be implemented in some embodiments to predict a future load shape from the encoded data, and predicting daily consumption from the predicted load shape and an estimate of daily total consumption.