Building Energy Efficiency Analysis and Diagnosis Using Integrated Image Processing and Thermal Imaging Technologies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the continuous growth of global energy demand and escalating environmental issues, enhancing building energy efficiency has become a critical challenge for many countries.Buildings, as major energy consumers, require precise energy efficiency analysis and diagnosis to achieve energy conservation and emission reduction goals.In recent years, the application of image processing and thermal imaging technologies in building energy efficiency analysis has become increasingly widespread.These technologies provide accurate energy efficiency assessments, aiding in the identification and resolution of energy efficiency issues within buildings.However, existing methods face numerous challenges in handling complex thermal imaging data and segmenting energy efficiency states, often failing to comprehensively reflect the actual energy performance of buildings.This paper proposes a novel approach to building energy efficiency analysis and diagnosis by integrating image processing and thermal imaging technologies.The approach comprises two main components.First, a selection search algorithm tailored for building infrared thermal images is introduced to enhance the precision and efficiency of thermal image processing.Second, a new method for segmenting building energy efficiency states is proposed, utilizing the (SHapley Additive exPlanations) SHAP attribution clustering algorithm to provide a more comprehensive and accurate evaluation of building energy performance.These advancements address the limitations of existing methods and offer new technical means for building energy efficiency analysis.The proposed approach not only improves the precision of energy efficiency assessments but also has significant application value and potential for widespread adoption.This research contributes to the ongoing efforts in energy conservation and provides a robust framework for future studies in building energy efficiency diagnostics.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it