Predicting whole-life carbon emissions for buildings using different machine learning algorithms: A case study on typical residential properties in Cornwall, UK
Predicting whole-life carbon emissions for buildings using different machine learning algorithms: A case study on typical residential properties in Cornwall, UK
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使用不同的机器学习算法预测建筑物的全寿命碳排放：英国康沃尔典型住宅物业的案例研究
DOI：
10.1016/j.apenergy.2023.122472
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发表时间：
2024-03
期刊：
影响因子：
11.2
通讯作者：
Lin Zheng;Markus Mueller;Chunbo Luo;Xiaoyu Yan
中科院分区：
文献类型：
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作者：
Lin Zheng;Markus Mueller;Chunbo Luo;Xiaoyu Yan
全寿命碳排放（WLCE）研究对于评估建筑物的环境影响和促进可持续设计实践至关重要。然而，用于估计WLCE的现有方法是耗时且数据密集的，限制了它们在早期建筑设计阶段的有用性。针对这一点，本研究引入了一种新的方法，通过利用各种机器学习算法来预测建筑物的WLCE和WLCE强度（按建筑面积归一化）。为了评估机器学习算法的适用性，我们进行了一项涉及十种算法的实验来构建预测模型。这些模型使用来自英国康沃尔郡150个典型住宅物业的数据进行训练，沿着从全面调查中获得的28个特征，包括地板面积，供暖类型和居住者特征。这十种算法包括多元线性回归，以及非线性算法，如决策树，随机森林。性能评价指标，如决定系数（R2），平均绝对误差（MAE），均方误差（MSE），均方根误差（RMSE），和经过的时间，采用。我们的研究通过展示机器学习模型在预测建筑WLCE方面的有效性为该领域做出了贡献。我们发现，所有测试的机器学习算法都有能力预测WLCE和WLCE强度，非线性模型优于线性模型，随机森林（RF）模型在准确性，稳定性和效率方面表现出上级性能。这项研究鼓励将生命周期研究纳入早期设计阶段，即使在紧张的建筑设计时间表中，也为建筑师和设计师提供实用指导。此外，这些结果也使广泛的利益相关者受益，不仅是建筑师，还有工程师，政策制定者和生命周期评估（LCA）研究人员，有助于在建筑行业内推进数据驱动的可持续发展方法。
Whole-life carbon emissions (WLCE) studies are critical in assessing the environmental impact of buildings and promoting sustainable design practices. However, existing methods for estimating WLCE are time-consuming and data-intensive, limiting their usefulness in the early building design stages. In response to this, this research introduces a novel approach by harnessing various machine learning algorithms to predict WLCE and WLCE intensity (normalised by floor area) for buildings. To evaluate the suitability of machine learning algorithms, we conducted an experiment involving ten algorithms to build the prediction models. These models were trained using data from 150 typical residential properties in Cornwall, UK, along with 28 features obtained from a comprehensive survey, including floor area, heating type, and occupant characteristics. The ten algorithms include Multiple Linear Regression, and non-linear algorithms such as Decision Tree, Random Forest. Performance evaluation metrics, such as coefficient of determination (R2), mean absolute error (MAE), means squared error (MSE), root-mean-square error (RMSE), and elapsed time, were employed. Our research contributes to the field by showcasing the effectiveness of machine learning models in predicting building WLCE. We reveal that all the tested machine learning algorithms have the capability to predict WLCE and WLCE intensity, non-linear models outperform linear ones, and the Random Forest (RF) model demonstrates superior performance in terms of accuracy, stability, and efficiency. This research encourages the integration of life cycle studies into the early design stage, even within tight building design schedules, offering practical guidance to architects and designers. Furthermore, these results also benefit a wide range of stakeholders, not only the architects but also the engineers, policymakers, and life cycle assessment (LCA) researchers, contributing to the advancement of data-driven sustainability approaches within the building sector.