Exploring the influence of urban context on building energy retrofit performance: A hybrid simulation and data-driven approach
Exploring the influence of urban context on building energy retrofit performance: A hybrid simulation and data-driven approach
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DOI：
10.1016/j.adapen.2021.100038
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发表时间：
2021-08
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通讯作者：
Alex Nutkiewicz;Benjamin Choi;Rishee K. Jain
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作者：
Alex Nutkiewicz;Benjamin Choi;Rishee K. Jain
城市是实现世界可持续能源目标的一个组成部分。具体而言，已实施改造，以提高建筑部门的能源效率并减少碳排放。最近的模拟，降阶和数据驱动的方法已被用来预测当前的城市建筑物的能源消耗。然而，这些努力在评估未来改造的潜在影响方面受到限制，因为它们无法解释可能影响城市建筑能源性能的建筑物间能源相互作用。为了克服这些限制，我们扩展了以前开发的混合数据驱动的城市能源模拟（DUE-S）模型，该模型利用建筑能源模拟和深度学习模型，现在预测各种建筑能源改造对城市多个时空尺度的影响。我们评估这种方法的案例研究的29个密集共处的建筑物在市中心的萨克拉门托，加州，美国。我们的研究结果表明，考虑到城市环境，改造对单个建筑的影响可以增加7.4%，因为它们也会影响周围环境的用电量。最后，我们展示了DUE-S如何提供有关如何选择建筑物进行改造的见解，以获得潜在的复合节能效果。我们开发了一个贪婪的优化算法，最大限度地减少所需的改造，以实现最大的节能在整个城市研究领域的数量。因此，这项工作强调了灵活的城市能源预测模型（如DUE-S）如何帮助各种具有城市意识的利益相关者（包括建筑师、工程师、规划师和政策制定者）做出与能源相关的决策。
Cities are an integral part to meeting the world's sustainable energy goals. Specifically, retrofits have been implemented to improve energy efficiency and reduce carbon emissions in the buildings sector. Recent simulation, reduced-order, and data-driven approaches have been used to predict the current energy consumption of urban buildings. However, these efforts are limited in their ability to evaluate potential impacts of future retrofits as they are unable to account for inter-building energy interactions that can influence urban building energy performance. To overcome these limitations, we extend a previously developed hybrid data-driven urban energy simulation (DUE-S) model that leverages building energy simulations and deep learning models by now predicting the impact of various building energy retrofits on multiple spatiotemporal scales across a city. We evaluate this approach on a case study of 29 densely co-located buildings in downtown Sacramento, California, USA. Our results indicate that accounting for urban context can compound the impact of retrofits on individual buildings by up to 7.4% as they also influence the electricity use of their surroundings. Finally, we show how DUE-S can provide insights on how to select buildings for retrofit that captures a potential compounding energy savings effect. We develop a greedy optimization algorithm that minimizes the number of required retrofits needed to achieve maximal energy savings across an urban study area. As a result, this work underscores how a flexible urban energy prediction model such as DUE-S can help inform energy-related decisions for a variety of urban-minded stakeholders including architects, engineers, planners, and policymakers.