Water-energy benchmarking and predictive modeling in multi-family residential and non-residential buildings
Water-energy benchmarking and predictive modeling in multi-family residential and non-residential buildings
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DOI：
10.1016/j.apenergy.2020.116074
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发表时间：
2021
期刊：
影响因子：
11.2
通讯作者：
Matthew Frankel;Lu Xing;Connor Chewning;L. Sela
中科院分区：
文献类型：
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作者：
Matthew Frankel;Lu Xing;Connor Chewning;L. Sela
随着气候变化的威胁随着城市人口的持续增加而增加，确保获得水和能源资源的必要性变得更加关键。在城市环境中水-能源关系的背景下，这项工作解决了目前在了解耦合的水和能源需求模式方面的差距，并揭示了不同类型建筑的水和能源利用之间的明显差异。这项研究提出了一种数据驱动的方法来确定基本的水和能源需求概况，将建筑物分类为具有相似水和能源使用的组，并预测其需求。将聚类问题归结为两阶段聚类集成问题，采用多种不同设置的聚类方法，然后将数据局部视图的结果组合在一起，以实现划分之间的一致性。确定了影响水和能源消耗的驱动因素，开发了参数和非参数预测模型，并利用高和低时间数据分辨率进行了比较。对异质建筑进行的聚类分析表明，异质建筑的用水和能源消耗模式并不完全以一般建筑特征为特征。对预测模型的分析表明，与参数模型相比，总体非参数模型提供了更好的水和能源预测，具有高和低数据分辨率的模型提供了可比较的需求预测。这项研究的结果突出了数据驱动建模的价值，它可以揭示对使用模式的有意义的洞察，并对建筑物的性能进行基准测试，以提供有意义的比较措施，以促进多公用事业管理。总体而言，这项研究概述的方法为在城市地区建立更大的复原力提供了又一步，为未来人口和气候的变化做好准备。
As the threat of climate change grows alongside a continual increase in urban population, the need to ensure access to water and energy resources becomes more crucial. In the context of the water-energy nexus in urban environments, this work addresses current gaps in understanding of coupled water and energy demand patterns and reveals apparent dissimilarities between utilization of water and energy resources for heterogeneous buildings. This study proposes a data-driven approach to identify fundamental water and energy demand profiles, cluster buildings into groups exhibiting similar water and energy use, and predict their demand. The clustering problem was cast as a two-stage cluster ensemble problem, in which several clustering methods with different settings were employed, and then the results obtained from partial view of the data were combined to achieve consensus among the partitionings. The influential drivers for water and energy consumption were identified, parametric and non-parametric prediction models were developed and compared, utilizing high and low temporal data resolution. The clustering analysis performed in this work revealed that water and energy consumption patterns of heterogeneous buildings are not exclusively characterized by general building characteristics. Analysis of the predictive models showed that an overall non-parametric model provides better predictions for water and energy compared with parametric models and that models with high and low data resolution provide comparable demand predictions. The results of this study highlight the value of data-driven modeling for revealing meaningful insights into usage patterns and benchmarking buildings’ performance to provide a meaningful measure of comparison to facilitate multi-utility management. Overall, the methods outlined in this study provide another step towards building greater resiliency within urban areas in preparation for future changes in population and climate.