Water-energy benchmarking and predictive modeling in multi-family residential and non-residential buildings

摘要

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 pro-files, 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.