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Prediction Model of Household Carbon Emission in Old Residential Areas in Drought and Cold Regions Based on Gene Expression Programming

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Publicado en:Buildings vol. 15, no. 14 (2025), p. 2462-2482
Autor principal: Chen, Shiao
Otros Autores: Gao Yaohui, Dai Zhaonian, Ren, Wen
Publicado:
MDPI AG
Materias:
Carbon content
Cold
Datasets
Correlation analysis
Neutrality
Back propagation networks
Residential areas
Urban renewal
Feature selection
International organizations
Energy consumption
Prediction models
Cold regions
Gene expression
Fossil fuels
Carbon
Root-mean-square errors
Algorithms
Emissions
Retrofitting
Neighborhoods
Optimization algorithms
Mean square errors
Population
Green buildings
Consumption patterns
Drought
Aging
Evolutionary algorithms
Adaptive algorithms
Machine learning
Neural networks
Energy efficiency
Aridity
Acceso en línea:Citation/Abstract
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Resumen:To support the national goals of carbon peaking and carbon neutrality, this study proposes a household carbon emission prediction model based on Gene Expression Programming (GEP) for low-carbon retrofitting of aging residential areas in arid-cold regions. Focusing on 15 typical aging communities in Kundulun District, Baotou City, a 17-dimensional dataset encompassing building characteristics, demographic structure, and energy consumption patterns was collected through field surveys. Key influencing factors (e.g., electricity usage and heating energy consumption) were selected using Pearson correlation analysis and the Random Forest (RF) algorithm. Subsequently, a hybrid prediction model was constructed, with its parameters optimized by minimizing the root mean square error (RMSE) as the fitness function. Experimental results demonstrated that the model achieved an R2 value of 0.81, reducing RMSE by 77.1% compared to conventional GEP models and by 60.4% compared to BP neural networks, while significantly improving stability. By combining data dimensionality reduction with adaptive evolutionary algorithms, this model overcomes the limitations of traditional methods in capturing complex nonlinear relationships. It provides a reliable tool for precision-based low-carbon retrofits in aging residential areas of arid-cold regions and offers a methodological advance for research on building carbon emission prediction driven by urban renewal.
ISSN:2075-5309
DOI:10.3390/buildings15142462
Fuente:Engineering Database