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

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.