Energy Consumption Pattern Monitoring via Parametric Model Comparison
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Solution Overview
Problem
Existing energy consumption monitoring systems lack the ability to efficiently detect statistically significant changes in energy consumption patterns, making it difficult for energy consumers to manage their energy usage effectively and identify cost-effective changes.
Innovation Solution
A system and method that uses a load monitoring server to define influencing drivers and create models of energy consumption, employing linear regression and piecewise linear models to predict energy usage based on independent variables, and alerting consumers to statistically significant changes by comparing baseline and updated models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated monitoring systems are implemented to detect changes in energy consumption patterns, then energy management efficiency is improved, but system complexity and implementation cost increase
Solution Approach 1:
The system performs automated model generation and comparison without requiring manual intervention. The load monitoring server automatically creates baseline models from historical data, generates updated models from current data, compares them to detect changes, and identifies statistically significant deviations. This self-service approach improves energy management efficiency while keeping operational complexity manageable.
Solution Approach 2:
The system pre-generates baseline models from historical energy consumption data before monitoring begins. By having the baseline model ready in advance, the system can immediately compare current consumption patterns against established baselines, enabling rapid detection of changes without requiring complex real-time analysis setup.
2Measurement precision
If statistical analysis methods are used to detect significant changes in energy patterns, then measurement precision is improved, but computational requirements and processing time increase
Solution Approach 1:
The system focuses statistical analysis only on the most significant model parameters rather than performing exhaustive analysis on all possible parameters. By identifying and analyzing only the key parameters that contribute most to energy consumption patterns, the system achieves high measurement precision for change detection while avoiding the computational overhead of complete parameter analysis.
3Loss of energy
If detailed modeling of energy consumption patterns is performed, then energy cost management is improved, but data processing requirements and system resource consumption increase
Solution Approach 1:
The system extracts and analyzes only the essential features and patterns from energy consumption data that are most relevant for cost management. Rather than processing all raw data in detail, the system identifies key driver variables and their relationships, modeling only the critical factors that influence energy costs. This selective approach improves energy cost management while reducing the computational resources and energy required for data processing.
Data Source
AI summary
A process for detecting statistically significant changes in energy consumption patterns by monitoring for changes in the parameters to a parametric energy model. Two parametric models of energy consumption are created: the first being a model providing an initial base line of energy consumption, the second being a test model to be compared to the initial base model. Statistically significant changes are detected by using a difference score that compares the parameters of two models along with the uncertainties of each parameter to determine whether the differences in the parameters of each model indicate a statistically significant deviation in the energy consumption pattern.