City-scale energy modeling to assess impacts of extreme heat on electricity consumption and production using WRF-UCM modeling with bias correction
City-scale energy modeling to assess impacts of extreme heat on electricity consumption and production using WRF-UCM modeling with bias correction
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发表时间：
2021
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通讯作者：
E. Jahani;Soham Vanage;D. Jahn;Kristen S. Cetin;W. Gallus
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作者：
E. Jahani;Soham Vanage;D. Jahn;Kristen S. Cetin;W. Gallus
城市规模的建筑物的能源消耗受建筑物所在地的天气条件的影响很大。因此，拥有适当的天气数据对于提高城市级能源消费和需求预测的准确性非常重要。通常，来自最近的机场或军事基地的当地气象站数据被用作建筑物能量模型的输入。然而，这些位置的天气数据通常与城市建筑物所经历的当地天气状况不同，特别是考虑到大多数地面气象站远离许多城市地区。使用天气研究和预报模式（WRF）与城市冠层模式（UCM）相结合，提供了预测天气条件更局部变化的手段。然而，尽管在气候模拟方面取得了进展，但在这些模拟中观察到地面观测和模式结果的系统差异。在这项研究中，WRF-UCM模式的结果和数据之间的比较，从40个地面气象站在奥斯汀，德克萨斯州进行评估现有的系统差异。通过迭代过程进行模型验证，其中调整输入参数以获得与测量数据的最佳拟合。为了解释剩余的系统误差，实施了具有空间和时间偏差校正的统计方法。该方法通过识别系统误差的统计特性和应用多种偏差校正技术，提高了WRF-UCM模式结果的质量。
The energy consumption of buildings at the city scale is highly influenced by the weather conditions where the buildings are located. Thus, having appropriate weather data is important for improving the accuracy of prediction of city-level energy consumption and demand. Typically, local weather station data from the nearest airport or military base is used as input into building energy models. However, the weather data at these locations often differs from the local weather conditions experienced by an urban building, particularly considering most ground-based weather stations are located far from many urban areas. The use of the Weather Research and Forecasting Model (WRF) coupled with an Urban Canopy Model (UCM) provides means to predict more localized variations in weather conditions. However, despite advances made in climate modeling, systematic differences in ground-based observations and model results are observed in these simulations. In this study, a comparison between WRF-UCM model results and data from 40 ground-based weather station in Austin, TX is conducted to assess existing systematic differences. Model validations was conducted through an iterative process in which input parameters were adjusted to obtain to best possible fit to the measured data. To account for the remaining systemic error, a statistical approach with spatial and temporal bias correction is implemented. This method improves the quality of the WRF-UCM model results by identifying the statistic properties of the systematic error and applying several bias correction techniques.