Resilient and Sustainable Urban and Energy Systems
Resilient and Sustainable Urban and Energy Systems
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弹性和可持续的城市和能源系统
DOI：
10.1061/9780784482445.059
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Jain, Rishee K.
中科院分区：
文献类型：
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作者：
Roth, Jonathan;Bailey, Aimee;Choudhary, Sonika;Jain, Rishee K.
了解城市能源消耗的时空分布，对于发现潜在的节能机会和规划新的可再生能源和综合区域能源系统至关重要。以前分析城市建筑能源使用的工作在很大程度上局限于在颗粒时间尺度（即每小时或更少）上对单个建筑进行建模，或者在每年的时间尺度上对整个城市建筑进行建模。虽然这些分析是有价值的，但它们缺乏空间和时间颗粒模型，限制了它们在综合区域能源系统规划和设计中的适用性。本文提出了一个新的城市建筑能源模型，该模型仅使用公开可用的数据为纽约市（NYC）的建筑库存生成小时需求概况。首先，我们利用机器学习模型从公开可用的年度能源使用数据的建筑物子集中预测纽约市整个建筑存量的年度能源消耗。我们使用纽约独立系统运营商（NYISO）的全市电力数据验证了模型的这一部分。结果表明，随机森林具有最佳的建筑水平预测精度，平均对数平方误差为0.293。接下来，我们应用一种新的优化算法，利用能源部的商业和住宅模拟建筑参考集，以及随机森林模型预测的年能源值，构建时间粒度小时剖面。结果表明，与纽约市的整体每小时电力概况相比，我们能够实现约10%的错误率（MAPE）。此外，我们发现我们的迭代方法表明，随着建筑物被添加到聚合剖面中，错误率会降低，这强调了应用我们提出的方法来建模整个城市的建筑物存量而不是单个建筑物的优点。最后，我们提出的方法迈出了城市建筑能源使用的大尺度空间和高粒度时间表征的第一步。
Understanding the spatial and temporal distribution of energy consumption in cities is critical to facilitate the identification of potential energy saving opportunities and planning of new renewable and integrated district energy systems. Previous work analyzing urban building energy usage has been largely limited to either modeling of individual buildings at granular temporal scales (ie, hourly or less) or an entire stock of urban buildings at the yearly temporal scale. While such analyses are valuable, their lack of both spatial and temporal granular modeling limits their applicability in planning and design of integrated district energy systems. This paper proposes a new urban building energy model that produces hourly demand profiles for the building stock of New York City (NYC) using only open publicly available data. First, we utilize a machine learning model to predict annual energy consumption of NYC’s entire building stock from a subset of buildings that have publicly available annual energy usage data. We validate this part of the model using city-wide electricity data from New York Independent System Operator (NYISO). Results show that random forests have the best building-level prediction accuracy with a mean log squared error of 0.293. Next, we apply a novel optimization algorithm to construct temporal granular hourly profiles using the Department of Energy's commercial and residential simulation building reference sets, and the predicted annual energy values from the random forests model. Results indicate that we are able to achieve an error rate of~ 10%(MAPE) in comparison to the overall hourly electricity profile of NYC. Moreover, we found that our iterative approach demonstrates that error rates diminish as buildings are added to the aggregated profile, which underscores the merits of applying our proposed method to model the entire building stock of a city rather than an individual building. In the end, our proposed method takes the first step of large-scale spatial and highly granular temporal characterization of urban building energy usage.