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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.
Shuling Liang (Wed,) studied this question.