Statistical representation for the <em>T</em><sub>2 m</sub> (° C), WS<sub>10 m</sub> (m s<sup>−1</sup>), and WD<sub>10 m</sub> (deg) at the urban and rural stations

<p><b>Table 2.</b>  Statistical representation for the <em>T</em><sub>2 m</sub> (° C), WS<sub>10 m</sub> (m s<sup>−1</sup>), and WD<sub>10 m</sub> (deg) at the urban and rural stations. RMSE is the root-mean square error, MAE is the mean absolute error, and MAPE is the mean absolute percentage error. </p> <p><strong>Abstract</strong></p> <p>Evaluation of built environment energy demand is necessary in light of global projections of urban expansion. Of particular concern are rapidly expanding urban areas in environments where consumption requirements for cooling are excessive. Here, we simulate urban air conditioning (AC) electric consumption for several extreme heat events during summertime over a semiarid metropolitan area with the Weather Research and Forecasting (WRF) model coupled to a multilayer building energy scheme. Observed total load values obtained from an electric utility company were split into two parts, one linked to meteorology (i.e., AC consumption) which was compared to WRF simulations, and another to human behavior. WRF-simulated non-dimensional AC consumption profiles compared favorably to diurnal observations in terms of both amplitude and timing. The hourly ratio of AC to total electricity consumption accounted for ~53% of diurnally averaged total electric demand, ranging from ~35% during early morning to ~65% during evening hours. Our work highlights the importance of modeling AC electricity consumption and its role for the sustainable planning of future urban energy needs. Finally, the methodology presented in this article establishes a new energy consumption-modeling framework that can be applied to any urban environment where the use of AC systems is prevalent.</p>