Energy Management System for Peak Load Reduction
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Solution Overview
Problem
Existing energy management systems fail to effectively tailor recommendations for reducing energy consumption based on individual users' specific peak usage times, leading to inefficient resource allocation and usage patterns.
Innovation Solution
A computer-implemented method that aggregates energy consumption data to identify peak users, generates load curves, and provides personalized energy efficiency reports to users, advising on times to reduce consumption, specifically targeting evening peak users with tailored advice.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If energy management systems provide general recommendations to all users, then implementation simplicity is maintained, but recommendation effectiveness and energy reduction impact are reduced
Solution Approach 1:
The patent segments users into different categories (peak users, off-peak users, evening peak users) based on their consumption patterns identified through load curve analysis. This segmentation enables targeted recommendations for specific user groups rather than generic advice for all users, thereby improving recommendation effectiveness and energy reduction impact while maintaining implementation feasibility through automated pattern recognition.
2Productivity
If energy management systems analyze individual user consumption patterns in detail, then recommendation personalization and effectiveness are improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system performs self-service by automatically analyzing user consumption data, generating load curves, identifying usage patterns, and categorizing users without requiring manual intervention. This automated self-analysis capability enables detailed pattern recognition and personalized recommendations while keeping operational complexity manageable through algorithm-driven processes rather than manual analysis.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring user consumption patterns, comparing actual usage against identified patterns, and providing targeted recommendations based on deviations. This feedback loop enables the system to adapt to changing user behaviors and improve recommendation effectiveness over time while maintaining systematic complexity through automated monitoring and adjustment protocols.
3Loss of energy
If energy management systems target specific peak users with personalized advice, then energy consumption during peak periods is reduced, but targeting precision and identification accuracy requirements increase
Solution Approach 1:
The system performs preliminary action by analyzing historical consumption data in advance to establish baseline load curves and identify user patterns before peak periods occur. This advance analysis enables the system to pre-identify peak users and prepare targeted recommendations, improving both identification accuracy and the timing of intervention to maximize energy reduction during peak periods.
Data Source
AI summary
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.