Article: Carbon comfort prediction and innovation enhancement for campus building clusters based on k-means clustering Journal: International Journal of Global Energy Issues (IJGEI) 2026 Vol.48 No.7 pp.85 - 108 Abstract: Combined with the bidirectional long short-term memory network, a temporal prediction model is constructed to characterise the dynamic evolution characteristics of carbon emissions and environmental comfort. On this basis, a multi-objective optimisation framework is established. The non-dominated sorting genetic algorithm II is adopted to solve the optimal Pareto frontier, thus realising the coordinated trade-off and dynamic regulation of energy consumption and comfort. On the premise of maintaining the indoor thermal-humidity environment within the optimal comfort range, the energy consumption of lighting and Heating, Ventilation, and Air Conditioning (HVAC) systems is successfully reduced by 21.4%. The optimisation of environmental quality significantly improves the cognitive status of researchers, with an estimated 11.5% increase in innovative work efficiency. The research findings confirm that reducing the carbon footprint of campuses can effectively empower scientific research and innovative productivity, providing a scientific paradigm for the refined management of green and smart parks. Inderscience Publishers - linking academia, business and industry through research

Title: Carbon comfort prediction and innovation enhancement for campus building clusters based on k-means clustering

Authors: Mengyi Li; Lin Wang

Addresses: Xi'an University of Architecture and Technology, Huaqing College, Xi'an, China ' Xi'an University of Architecture and Technology, Huaqing College, Xi'an, China

Abstract: Combined with the bidirectional long short-term memory network, a temporal prediction model is constructed to characterise the dynamic evolution characteristics of carbon emissions and environmental comfort. On this basis, a multi-objective optimisation framework is established. The non-dominated sorting genetic algorithm II is adopted to solve the optimal Pareto frontier, thus realising the coordinated trade-off and dynamic regulation of energy consumption and comfort. On the premise of maintaining the indoor thermal-humidity environment within the optimal comfort range, the energy consumption of lighting and Heating, Ventilation, and Air Conditioning (HVAC) systems is successfully reduced by 21.4%. The optimisation of environmental quality significantly improves the cognitive status of researchers, with an estimated 11.5% increase in innovative work efficiency. The research findings confirm that reducing the carbon footprint of campuses can effectively empower scientific research and innovative productivity, providing a scientific paradigm for the refined management of green and smart parks.

Keywords: improved k-means clustering; carbon comfort; bidirectional long short-term memory; multi-objective collaborative optimisation; innovative productivity; smart energy management.

DOI: 10.1504/IJGEI.2026.152146

International Journal of Global Energy Issues, 2026 Vol.48 No.7, pp.85 - 108

Received: 18 Dec 2025
Accepted: 30 Jan 2026
Published online: 09 Mar 2026 *

Title: Carbon comfort prediction and innovation enhancement for campus building clusters based on k-means clustering

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