Research on Energy Efficiency Evaluation System for Rural Houses Based on Improved Mask R-CNN Network

Liping He, Kun Gao, Yuan Jin, Zhechen Shen, Yane Li, Fang’ai Chi () and Meiyan Wang ()
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Liping He: College of Landscape Architecture, Zhejiang A&F University, Hangzhou 311300, China
Kun Gao: College of Landscape Architecture, Zhejiang A&F University, Hangzhou 311300, China
Yuan Jin: College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China
Zhechen Shen: College of Landscape Architecture, Zhejiang A&F University, Hangzhou 311300, China
Yane Li: College of Landscape Architecture, Zhejiang A&F University, Hangzhou 311300, China
Fang’ai Chi: College of Landscape Architecture, Zhejiang A&F University, Hangzhou 311300, China
Meiyan Wang: College of Landscape Architecture, Zhejiang A&F University, Hangzhou 311300, China

Sustainability, 2025, vol. 17, issue 3, 1-18

Abstract: This study addresses the issue of energy efficiency evaluation for rural residential buildings and proposes a method for facade recognition based on an improved Mask R-CNN network model. By introducing the Coordinate Attention (CA) mechanism module, the quality of feature extraction and detection accuracy is enhanced. Experimental results demonstrate that this method effectively recognizes and segments windows, doors, and other components on building facades, accurately extracting key information, such as their dimensions and positions. For energy consumption simulation, this study utilized the Ladybug Tool in the Grasshopper plugin, combined with actual collected facade data, to assess and simulate the energy consumption of rural residences. By setting building envelope parameters and air conditioning operating parameters, detailed calculations of energy consumption for different orientations, window-to-wall ratios, and sunshade lengths were performed. The results show that the improved Mask R-CNN network model plays a crucial role in quickly and accurately extracting building parameters, providing reliable data support for energy consumption evaluation. Finally, through case studies, specific energy-saving retrofit suggestions were proposed, offering robust technical support and practical guidance for energy optimization in rural residences.

Keywords: R-CNN algorithm; instance segmentation; convolutional neural network; energy efficiency evaluation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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