2019 CSCE Annual Conference - Laval (Greater Montreal)

2019 CSCE Annual Conference - Laval (Greater Montreal) Conference


Title
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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Author(s)
Ms. Elham Jahani, Iowa State University
Mr. Soham Vanage, Iowa State University
Mr. David Jahn, Iowa State University
Dr. Kristen Cetin, Iowa State University (Presenter)
Dr. William Gallus, Iowa State University
Mr. Phong Nguyen
Mr. Youngchan Jang, University of Michigan
Dr. Eunshin Byon
Dr. Lance Manuel, University of Texas at Austin
Abstract

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 validation was conducted through an iterative process in which input parameters were adjusted to obtain to best possible with 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 the bias correction techniques.