SynCity: Using open data to create a synthetic city of hourly building energy estimates by integrating data-driven and physics-based methods
SynCity: Using open data to create a synthetic city of hourly building energy estimates by integrating data-driven and physics-based methods
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DOI：
10.1016/j.apenergy.2020.115981
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发表时间：
2020-12-15
期刊：
影响因子：
11.2
通讯作者：
Jain, Rishee K.
中科院分区：
文献类型：
--
作者：
Roth, Jonathan;Martin, Amory;Jain, Rishee K.
城市官员越来越有兴趣了解建筑环境的空间和时间能源模式，以促进城市向低碳未来的过渡。在本文中，提出了一种新的增强城市建筑能耗模型（A-UBEM），该模型结合了数据驱动和基于物理的模拟方法，为城市中的每栋建筑产生合成的小时负荷曲线估计值，类似于每小时智能电表测量的数据。通过只使用公开可用的数据，一个可推广的两步过程的实施，其他城市类似的可用数据可以复制使用纽约市作为案例研究。步骤（1）使用监督机器学习算法估计城市中每栋建筑的年能源使用量。步骤（2）扩展了这些结果，并通过凸优化公式来利用基于物理的仿真模型，该凸优化公式最小化聚合的建筑物需求与观察到的全市每小时电力需求之间的平方差。步骤（1）的结果表明，随机森林算法表现最好，平均对数平方误差为0.293，而步骤（2）中的凸优化导致平均训练误差为6.11%的平均绝对百分比误差（MAPE）。为了验证所产生的负载曲线的稳定性，使用来自城市的建筑物的随机子集进行Monte Carlo模拟，其在每个模拟中产生平均6.41%MAPE的样本外误差。粒子群优化算法也探索使用蒙特卡洛模拟的结果，以评估该模型是否可以通过放宽某些限制，但边际误差减少，进一步证明了该模型的稳定性。总体而言，A-UBEM是仅使用开放数据创建高度粒度的城市规模合成小时负荷曲线的第一步。这种负荷曲线对于规划可持续城市和加速采用低碳分布式能源和区域能源系统是不可或缺的。
Cities officials are increasingly interested in understanding spatial and temporal energy patterns of the built environment to facilitate their city's transition to a low-carbon future. In this paper, a new Augmented-Urban Building Energy Model (A-UBEM) is proposed that combines data-driven and physics-based simulation methods to produce synthetic hourly load curve estimates for every building within a city-similar to data an hourly smart meter would measure. By using only publicly available data, a generalizable two-step process is implemented-that other cities with similar available data can replicate-using New York City as a case study. Step (1) estimates the annual energy use for every building in the city using supervised machine learning algorithms. Step (2) extends these results and leverages physics-based simulation models through a convex optimization formulation that minimizes the squared difference between the aggregated building demand and the observed city-wide hourly electricity demand. Results from step (1) show that the Random Forest algorithm performs best with a mean log squared error of 0.293, while the convex optimization in step (2) results in a mean training error of 6.11% mean absolute percentage error (MAPE). To validate the stability of the produced load curves, Monte Carlo simulations are conducted, using random subsets of buildings from the city , which produce an out-of-sample error averaging 6.41% MAPE across each simulation . Particle swarm optimization is also explored-using the results from the Monte Carlo simulation-to assess if the model could be improved by relaxing certain constraints, but marginal error reductions are found, further proving the stability of the proposed model. Overall, A-UBEM is a first step towards creating highly granular urban-scale synthetic hourly load curves solely using open data. Such load curves are integral for planning sustainable cities and accelerating the adoption of low-carbon distributed energy resources (DERs) and district energy systems.