CO2 Emission Estimation Model Using Multiple Regression for Print Jobs
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Solution Overview
Problem
Existing CO2 emission estimation systems for image forming apparatuses suffer from low accuracy in estimating CO2 emissions due to simplistic calculation methods that do not adequately consider various job settings and power consumption variations.
Innovation Solution
A CO2 emission estimation system that employs a multiple regression expression-based model, where the power consumption of the image forming apparatus is estimated using various explanatory variables related to job settings, such as print mode, layout, and medium type, and then multiplied by a CO2 emission factor to calculate the estimated CO2 emissions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a simplistic calculation method is used for CO2 emission estimation, then the calculation process is simple and fast, but the estimation accuracy is low
Solution Approach 1:
The patent transforms the CO2 emission estimation from a simple multiplication of power consumption by a fixed emission factor into a multiple regression model that considers multiple parameters simultaneously. The model uses explanatory variables including print mode, layout configuration, medium type, and other job settings to dynamically calculate power consumption, which is then multiplied by a CO2 emission factor. This parameter-based approach significantly improves estimation accuracy while maintaining computational efficiency through the use of pre-determined regression coefficients.
2Measurement precision
If a multiple regression model with multiple explanatory variables is used, then the estimation accuracy is improved, but the calculation complexity increases
Solution Approach 1:
The patent performs preliminary determination of regression coefficients and model structures before actual CO2 emission calculations. The multiple regression model with multiple explanatory variables (print mode, layout, medium type, etc.) is pre-configured with optimized coefficients. During runtime, the system only needs to input current job settings and apply the pre-determined model, which dramatically reduces computational complexity while maintaining high estimation accuracy.
3Measurement precision
If various job settings are considered in the estimation model, then the estimation comprehensiveness is improved, but the data processing complexity increases
Solution Approach 1:
The patent segments the CO2 emission estimation process into distinct components, each handled by specific explanatory variables in the multiple regression model. Different job settings (print mode, layout, medium type) are treated as separate variables with predetermined coefficients. This segmentation allows the system to comprehensively consider various job settings while simplifying data processing, as each variable can be independently evaluated and combined through the regression formula.
Data Source
AI summary
A CO2 emission estimation system includes a CO2 emission estimator. The CO2 emission estimator calculates an estimated value of CO2 emissions from an image forming apparatus when performing a job, using a CO2 emission estimation model as a formula for estimating the CO2 emissions and settings for the job. The CO2 emission estimation model is a formula where a multiple regression expression for determining an estimated value of a power consumption of the image forming apparatus when performing the job is multiplied by a CO2 emission factor. The CO2 emission estimation model has a plurality of explanatory variables each according to a different type of setting for the job.