Building thermal energy efficiency modeling based on light image inspection and super resolution algorithm in interior landscape design

Abstract

With the intensification of global climate change and energy crisis, the thermal efficiency of buildings has become increasingly important. Interior landscape design not only affects living comfort, but also plays a key role in building energy efficiency. Therefore, it is of great practical significance to explore the modeling method of building thermal energy efficiency based on optical image inspection and super resolution algorithm. The light image inspection technique is used to obtain the data of light and temperature distribution inside the building. The super-resolution algorithm of deep learning is used to process the acquired low-resolution image to improve the clarity and detail of the image. Combined with the physical characteristics of the building, the thermal efficiency model of the building is constructed and multi-dimensional analysis is carried out. The experimental results show that the combination of optical image inspection and super resolution algorithm can effectively improve the accuracy of building thermal efficiency modeling. Compared with traditional methods, the prediction error of the model is reduced, and the recommended optimization scheme performs better in terms of energy consumption in different interior landscape design schemes. Therefore, the building thermal efficiency modeling method based on optical image inspection and super resolution algorithm provides a new idea for interior landscape design. Through accurate thermal efficiency analysis, it can provide more scientific decision-making basis for architectural designers, so as to realize the sustainable development of buildings and maximize the energy efficiency.