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Big-data driven building retrofitting: An integrated Support Vector Machines and Fuzzy C-means clustering method
IOP Conference Series: Earth and Environmental Science Pub Date : 2020-11-21 , DOI: 10.1088/1755-1315/588/4/042013
Weizhuo Lu 1 , Kailun Feng 1, 2, 3, 4
Affiliation  

It has become a mainstream to use physical models to quantify expected energy savings from alternative retrofit methods and technologies. However, they are not suitable for predicting energy use of buildings when detailed and specified input parameters are unavailable. The overall purpose of the research is to support the stakeholders in taking decisions on refurbishments options when not all of physical information is available, in order to achieve the Swedish Energy Agency’s measurements of near-zero energy buildings. The research will transfer big data from Swedish Energy Performance Certificates for building retrofitting. A Support Vector Machines and Fuzzy C-means clustering (SVM-FCM) integrated machine learning algorithm is used directly to extract the case-specific knowledge from EPC big data regarding building characteristics and energy saving of retrofit measures. It enables to prioritize retrofit measures and compute their expected energy savings for buildings. This proposed data driven method is an attempt of taking advantage of big data for practical building retrofit selection.



中文翻译:

大数据驱动的建筑改造:一种集成的支持向量机和模糊 C 均值聚类方法

使用物理模型来量化替代改造方法和技术的预期节能已经成为主流。然而,当详细和指定的输入参数不可用时,它们不适合预测建筑物的能源使用。该研究的总体目的是支持利益相关者在无法获得所有物理信息的情况下就翻新选项做出决策,以实现瑞典能源署对近零能耗建筑的测量。该研究将把瑞典能源绩效证书中的大数据用于建筑改造。支持向量机和模糊 C 均值聚类 (SVM-FCM) 集成机器学习算法直接用于从 EPC 大数据中提取有关建筑特征和改造措施节能的案例特定知识。它可以优先考虑改造措施并计算其预期的建筑物节能。这种提出的数据驱动方法是利用大数据进行实际建筑改造选择的尝试。

更新日期:2020-11-21
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