Eco Score Analytics for Energy Program Targeting
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Solution Overview
Problem
Implementing information campaigns and educational programs for energy conservation is resource-intensive and inefficient, as not all targets have a propensity to favorably receive and use the information, leading to wastage in channeling resources to less effective recipients.
Innovation Solution
The Eco Score Analytics (ESA) system determines eco scores for potential targets based on attributes and parameters, allowing for precise selection of targets likely to participate in energy programs, using a factor profile initiator, scoring module, and campaign engine to optimize resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If information campaigns and educational programs are implemented for all potential targets, then energy conservation impact is improved, but resource consumption and cost increase significantly
Solution Approach 1:
The patent segments the population into distinct groups based on their propensity to respond to energy conservation information. By dividing potential targets into segments with different response probabilities, the system enables targeted campaigns that focus resources on high-propensity groups rather than uniformly distributing resources to all individuals, thereby improving energy conservation impact while reducing overall resource consumption.
Solution Approach 2:
The patent applies partial action by implementing information campaigns only for a selected subset of the population (those with high propensity to respond) rather than for everyone. This partial targeting approach maintains sufficient energy conservation impact by concentrating efforts on responsive individuals while avoiding the excessive resource consumption that would result from universal campaigning.
2Area of stationary object
If information campaigns are directed to all population segments, then coverage is improved, but waste of resources on low-propensity targets increases
Solution Approach 1:
The patent segments the population based on measured propensity characteristics to identify which segments are most likely to respond favorably to energy conservation information. This segmentation enables the system to maintain effective coverage of high-propensity groups while excluding low-propensity groups from targeted campaigns, thereby reducing resource waste without significantly compromising overall coverage of responsive individuals.
Solution Approach 2:
The patent applies local quality by directing different levels of information campaign resources to different population segments based on their specific propensity characteristics. High-propensity segments receive targeted attention and resources, while low-propensity segments receive minimal or no targeted resources. This localized resource allocation optimizes the balance between coverage and resource efficiency by matching resource intensity to segment-specific response potential.
3Measurement precision
If eco scores are calculated for all potential targets, then targeting precision is improved, but computational resources and time increase
Solution Approach 1:
The patent implements preliminary action by pre-calculating and storing propensity scores for all potential targets before campaigns are launched. These pre-computed scores serve as ready-to-use metrics for target selection, eliminating the need for complex real-time calculations during campaign deployment. This preliminary computation maintains high targeting precision while reducing the time and computational resources required during actual campaign execution.
Solution Approach 2:
The patent applies partial action by calculating detailed eco scores only for a selected subset of potential targets who are most likely to be included in campaigns, rather than performing exhaustive calculations for every individual in the population. This partial scoring approach maintains sufficient targeting precision for campaign selection while significantly reducing the computational burden and time requirements compared to universal scoring.
Data Source
AI summary
A system and method are configured to perform eco score analytics for an energy program associated with potential targets. A factor profile initiator determines categories and subcategories of parameters to be included in a model for the energy program based on attributes of the energy program. The model is built and used to determine eco scores for the potential targets. The eco scores estimate propensity of the potential target to participate in the energy program. A campaign engine determines ranking of the scores and a subset of the potential targets are selected as targets for the energy program based on the rankings.