Data-driven optimization of building layouts for energy efficiency
Data-driven optimization of building layouts for energy efficiency
复制标题
DOI：
10.1016/j.enbuild.2021.110815
复制
发表时间：
2020-07
期刊：
影响因子：
--
通讯作者：
A. Sonta;Thomas R. Dougherty;Rishee K. Jain
中科院分区：
文献类型：
--
作者：
A. Sonta;Thomas R. Dougherty;Rishee K. Jain
建筑物能源性能的主要驱动因素之一是居住者行为动力学。因此，建筑物居住者工作站的布局可能会影响能源消耗。在本文中，我们将介绍有关照明区域能源的区域级居住者动态，模拟基于这种关系的照明系统的能源消耗，并优化建筑物的布局的方法。该优化方法采用了基于聚类的方法和遗传算法，其目的是降低能耗。我们在一个案例研究中发现，非均匀行为（即，高多样性）与高度可控的照明系统的能量消耗正相关。我们还发现，通过数据驱动的模拟，朴素的基于聚类的优化和遗传算法（利用能源模拟引擎）产生的布局，减少了约5%的能源消耗相比，现有的布局的真实的办公空间由151个占用者。总体而言，这项研究表明了利用现有建筑布局的低成本动态设计作为减少能源使用的一种手段的优点。我们的工作通过新的非资本密集型干预措施，为在建筑环境中实现我们的可持续能源目标提供了另一条途径。
One of the primary driving factors in building energy performance is occupant behavioral dynamics. As a result, the layout of building occupant workstations is likely to influence energy consumption. In this paper, we introduce methods for relating lighting zone energy to zone-level occupant dynamics, simulating energy consumption of a lighting system based on this relationship, and optimizing the layouts of buildings. The optimization makes use of both a clustering-based approach and a genetic algorithm, and it aims to reduce energy consumption. We find in a case study that nonhomogeneous behavior (i.e., high diversity) among occupant schedules positively correlates with the energy consumption of a highly controllable lighting system. We additionally find through data-driven simulation that the naïve clustering-based optimization and the genetic algorithm (which makes use of the energy simulation engine) produce layouts that reduce energy consumption by roughly 5% compared to the existing layout of a real office space comprised of 151 occupants. Overall, this study demonstrates the merits of utilizing low-cost dynamic design of existing building layouts as a means to reduce energy usage. Our work provides an additional path to reach our sustainable energy goals in the built environment through new non-capital-intensive interventions.