Building Energy Efficiency Analysis and Diagnosis Using Integrated Image Processing and Thermal Imaging Technologies
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".