Predictive Energy Customer Segmentation via ML
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Solution Overview
Problem
Current energy utility segmentation methods rely on fixed rule-sets based on anecdotal experience, lacking predictive power and interpretability, and fail to effectively target energy efficiency program enrollment.
Innovation Solution
A predictive segmentation technique using machine learning to identify homogeneous sub-groups among energy consumers based on consumption and demographic data, optimizing pattern allocation to segments for enhanced enrollment likelihood.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If fixed rule-sets based on anecdotal experience are used for segmentation, then the segmentation process is simple and easy to implement, but the predictive power and effectiveness of targeting energy efficiency programs is reduced
Solution Approach 1:
The patent replaces manual, anecdotal segmentation rules with an automated machine learning system that uses decision trees and random forests algorithms. The system automatically processes consumption data, demographic information, and program enrollment history to generate predictive segments, eliminating the need for subjective rule creation while improving segmentation accuracy and predictive power.
Solution Approach 2:
The segmentation system performs self-service by automatically learning from historical data without requiring external expert intervention for rule creation. The machine learning models autonomously identify patterns in consumer behavior and generate segmentation rules based on actual enrollment outcomes, allowing the system to improve itself through continuous learning from program results.
2Reliability
If machine learning techniques are used to create predictive segments, then the predictive power and enrollment targeting effectiveness is improved, but the complexity of the segmentation process and data requirements increases
Solution Approach 1:
The patent applies segmentation by dividing the consumer population into distinct segments based on predicted enrollment likelihood. The machine learning models create homogeneous sub-groups with similar energy consumption patterns, demographic characteristics, and program response behaviors, enabling targeted communication strategies for each segment while managing complexity through structured classification.
Solution Approach 2:
The system performs preliminary action by pre-processing and preparing consumption data, demographic information, and program enrollment history before feeding it into the machine learning models. This includes data cleaning, feature engineering, and transformation into formats suitable for decision tree and random forest algorithms, reducing the complexity burden during the actual segmentation execution.
3Measurement precision
If detailed consumption data and demographic information are collected and processed, then the accuracy of predictive segmentation is improved, but the data processing requirements and computational resources increase
Solution Approach 1:
The patent extracts only the most relevant and predictive features from the available consumption data and demographic information. The machine learning models automatically identify and select key variables such as energy consumption patterns, appliance usage, household composition, and socioeconomic factors that most strongly predict program enrollment, filtering out redundant or less informative data elements to reduce processing volume while maintaining segmentation accuracy.
Data Source
AI summary
A computer system receives customer records listing customer attributes and an adoption status of the customer, such as whether the customer has enrolled in a particular energy efficiency program. An initial set of patterns are identified among the customer records, such as according to a decision tree. The initial set is pruned to obtain a set of patterns that meet minimum support and effectiveness and maximum overlap requirements. The patterns are assigned to segments according to an optimization algorithm that seeks to maximize the minimum effectiveness of each segment, where the effectiveness indicates a number of customers matching the pattern of each segment that have positive adoption status. The optimization algorithm may be a bisection algorithm that evaluates a linear-fractional integer program (LFIP-F) to iteratively approach an optimal distribution of patterns.