Building carbon emission prediction plays an irreplaceable role in low-carbon economy development, public health protection and environmental sustainability. It is significant to identify influential factors mainly contributed to building emission and predict emission accurately in order to harnessthe growth from the source. In this paper, 11 influencing factors of building carbon emission are identified and a support vector regression (SVR) prediction model is proposed to forecast building carbon emission considering improvement the prediction accuracy, generalization, and robustness. In the SVR model, parameters are optimized by particle swarm optimization (PSO) algorithm with the aim to improve performance. Cases in Shanghai’s building sector are adopted to demonstrate practical applications of the proposed PSO-SVR prediction model. The results indicate that the presented prediction system has an outstanding performance in forecasting building carbon emission under multi-criteria evaluation. Furthermore, compared to the results from other four prediction models (e.g., linear regression, decision tree), it is shown that PSO-SVR model can achieve higher accuracy (e.g., improvement average of 1.01% R2 under training subset), better generalization (e.g., improvement average of 19.89% R2 under testing subset), and better robustness (e.g., improvement average of 18.93% R2 under different levels of noise intensity).

中文翻译：

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多准则评价下的PSO-SVR方法构建碳排放预测模型

建筑碳排放预测在低碳经济发展、公共卫生保护和环境可持续发展等方面具有不可替代的作用。明确影响建筑排放的主要影响因素，准确预测排放，从源头上驾驭增长具有重要意义。本文识别了11个影响建筑碳排放的因素，提出了一种支持向量回归（SVR）预测模型来预测建筑碳排放，同时提高预测的准确性、泛化性和鲁棒性。在 SVR 模型中，通过粒子群优化 (PSO) 算法优化参数，以提高性能。上海建筑行业的案例被用来展示所提出的 PSO-SVR 预测模型的实际应用。结果表明，所提出的预测系统在多标准评估下预测建筑碳排放方面具有突出的性能。此外，与其他四种预测模型（例如线性回归、决策树）的结果相比，PSO-SVR 模型可以实现更高的准确率（例如，训练子集下平均提高 1.01% R2），更好的泛化（例如，在测试子集下平均提高 19.89% R2），以及更好的鲁棒性（例如，在不同噪声强度水平下提高平均 18.93% R2）。