Meteorological Data Processing Method for Energy-Saving Design of Intelligent Buildings Based on the Compressed Sensing Reconstruction Algorithm

Jingjing Jia, Chulsoo Kim, Chunxiao Zhang, Mengmeng Han and Xiaoyun Li ()
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Jingjing Jia: Department of Industrial Design, Pukyong National University, 45, Yongso-ro, Nam-Gu, Busan 48513, Gyeonggi-do, Republic of Korea
Chulsoo Kim: Department of Industrial Design, Pukyong National University, 45, Yongso-ro, Nam-Gu, Busan 48513, Gyeonggi-do, Republic of Korea
Chunxiao Zhang: Department of Industrial Design, Pukyong National University, 45, Yongso-ro, Nam-Gu, Busan 48513, Gyeonggi-do, Republic of Korea
Mengmeng Han: Department of Industrial Design, Pukyong National University, 45, Yongso-ro, Nam-Gu, Busan 48513, Gyeonggi-do, Republic of Korea
Xiaoyun Li: Department of Art and Media, Jinzhong College of Information, No. 8 Xueyuan Road, Taigu District, Jinzhong 030800, China

Sustainability, 2025, vol. 17, issue 4, 1-23

Abstract: With the increasingly severe problems of global climate change and resource scarcity, sustainable development has become an important issue of common concern in various industries. The construction industry is one of the main sources of global energy consumption and carbon emissions, and sustainable buildings are an effective way to address climate change and resource scarcity. Meteorological conditions are closely related to building energy efficiency. Therefore, the research is founded upon a substantial corpus of meteorological data, employing a compressed sensing reconstruction algorithm to supplement the absent meteorological data, and subsequently integrating an enhanced density peak clustering algorithm for data mining. Finally, an intelligent, sustainable, building energy-saving design platform is designed based on this. The research results show that in the case of random defects in monthly timed data that are difficult to repair, the reconstruction error of the compressed sensing reconstruction algorithm is only 0.0403, while the improved density peak clustering algorithm has the best accuracy in both synthetic and real datasets, with an average accuracy corresponding to 0.9745 and 0.8304. Finally, in the application of the intelligent, sustainable, building energy-saving design platform, various required information such as HVAC data energy-saving design parameters, cloud cover, and temperature radiation are intuitively and fully displayed. The above results indicate that the research method can effectively explore the potential valuable information of sustainable building energy-saving design, providing a reference for the design of sustainable buildings and climate analysis.

Keywords: compressed sensing reconstruction algorithm; sustainable building; energy-saving design; meteorological data (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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