Energy Load Modeling Across Distinct Operating Modes
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Solution Overview
Problem
Physical systems with distinct operating modes present a challenge for modeling, as existing methods like linear regression models fail to accurately predict behavior across different regimes, leading to inefficiencies in energy management and monitoring.
Innovation Solution
A system and method that uses a load monitoring server to define influencing drivers and partition variables, creating separate models for each discrete value of the partition variables to accurately predict energy load behavior, allowing for efficient energy management and monitoring across different operating conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a single linear regression model is used to model physical systems with distinct operating modes, then the model structure is simple and easy to implement, but the prediction accuracy deteriorates across different operating regimes
Solution Approach 1:
The patent divides the physical system into multiple operating modes or regimes based on observed behavior patterns. Separate linear regression models are constructed for each operating mode, allowing each model to be optimized for its specific regime while maintaining the simplicity and interpretability of linear models within each segment.
2Measurement precision
If separate linear models are created for each operating mode, then the prediction accuracy improves for each regime, but the overall system complexity increases
Solution Approach 1:
The patent implements a dynamic model selection mechanism that automatically identifies the current operating mode based on real-time system behavior and selects the appropriate pre-trained linear model for prediction. This dynamic approach allows the system to switch between multiple simple linear models rather than using one complex model, maintaining computational efficiency while improving accuracy across different operating regimes.
Data Source
AI summary
A system, method, and computer program product for predicting operation for physical systems with distinct operating modes uses observable qualities of the system to predict other qualities of the system. Independent variables including temperature or production volume are observed to determine the degree to which a dependent modeled variable, including energy load, is influenced. Partition variables representing operating conditions of the dependent variables are defined as discrete values. Reference datasets with coincident values of the dependent variable, independent variable, and partition variables are received, and models are created for each discrete value of the partition variables in the reference dataset. Each model is populated with the values of the dependent variable and the independent variable. The dependent variable is modeled as a function of the independent variable. Model accuracy is evaluated by processing new input data to generate output data that includes values of the coincident dependent variable, the independent variable, and the partition variable from the input dataset.