Learning-Model Evaluation of Energy Saving Under Variable Conditions
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Solution Overview
Problem
Current evaluation methods for energy saving effects in facilities, such as plants, are unreliable as they fail to accurately account for variations in operation environments and performance indicators across different periods, leading to inconsistent energy saving effect calculations.
Innovation Solution
An evaluation apparatus that estimates performance based on past operations by generating a learning model using environmental and performance data from a specified learning target period, allowing for relative evaluation of actual performance in an evaluation target period, thereby offsetting environmental differences and providing accurate energy saving effect quantification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional evaluation methods are used to calculate energy saving effects, then the calculation process is simple, but the reliability and accuracy of the energy saving effect calculation deteriorates due to failure to account for operation environment variations
Solution Approach 1:
The patent introduces a learning model as an intermediary between the evaluation target and the evaluation criteria. This learning model is trained using historical operation data to establish the relationship between operation environments and performance indicators, thereby mediating the evaluation process and enabling accurate assessment while accounting for environmental variations
Solution Approach 2:
The patent performs preliminary training of the learning model using historical operation data before conducting the actual energy saving effect evaluation. This preliminary action establishes the baseline relationship between operation environments and performance indicators, enabling accurate subsequent evaluations
2Measurement precision
If performance indicators are directly compared across different periods without environmental adjustment, then the evaluation process is straightforward, but the measurement precision deteriorates due to environmental differences
Solution Approach 1:
The patent changes the parameters of the learning model based on operation environment parameters. When evaluating performance indicators under different operation environments, the learning model parameters are adjusted to reflect the specific environmental conditions, thereby enabling accurate comparisons while accounting for environmental variations
3Measurement precision
If a learning model is trained using historical data to account for environmental variations, then the evaluation accuracy improves, but the complexity of data processing and model generation increases
Solution Approach 1:
The learning model performs self-service by automatically learning the relationship between operation environments and performance indicators from historical data. The model autonomously adjusts its parameters and structure based on the training data, reducing the need for manual intervention and complex data processing procedures
Data Source
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AI summary
[Solution] Provided is an evaluation apparatus comprising: an environmental data acquisition unit configured to acquire environmental data indicating an operation environment of equipment; a performance data acquisition unit configured to acquire performance data indicating an operation performance of the equipment; an estimation unit configured to estimate an operation performance on an operation basis in a learning target period under an operation environment in an evaluation target period, based on the environmental data and the performance data in the learning target period; an evaluation unit configured to calculate an indicator that relatively evaluates an actually measured value of an operation performance in the evaluation target period with respect to an estimated value of the estimated operation performance; and an output unit configured to output the indicator.