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  1. Accurate and efficient power demand forecasting in urban settings is essential for making decisions related to planning, managing and operations in electricity supply. This task, however, is complicated due to many sources of uncertainty such as due to the variation in weather conditions and household or other needs that influence the inherent stochastic and nonlinear characteristics of electricity demand. Due to the modeling flexibility and computational efficiency afforded by it, a Gaussian process model is employed in this study for energy demand prediction as a function of temperature. A Gaussian process model is a Bayesian non-parametric regression method that models data using a joint Gaussian distribution with mean and covariance functions. The selected mean function is modeled as a polynomial function of temperature, whereas the covariance function is appropriately selected to reflect the actual data patterns. We employ real data sets of daily temperature and electricity demand from Austin, Texas, USA to assess the effectiveness of the proposed method for load forecasting. The accuracy of the model prediction is evaluated using metrics such as mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE) and 95% confidence interval (95% CI). A numerical study undertaken demonstrates that the proposed method has promise for energy demand prediction. 
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  2. Severe winds produced by thunderstorm downbursts pose a serious risk to the structural integrity of wind turbines. However, guidelines for wind turbine design (such as the International Electrotechnical Commission Standard, IEC 61400-1) do not describe the key physical characteristics of such events realistically. In this study, a large-eddy simulation model is employed to generate several idealized downburst events during contrasting atmospheric stability conditions that range from convective through neutral to stable. Wind and turbulence fields generated from this dataset are then used as inflow for a 5-MW land-based wind turbine model; associated turbine loads are estimated and compared for the different inflow conditions. We first discuss time-varying characteristics of the turbine-scale flow fields during the downbursts; next, we investigate the relationship between the velocity time series and turbine loads as well as the influence and effectiveness of turbine control systems (for blade pitch and nacelle yaw). Finally, a statistical analysis is conducted to assess the distinct influences of the contrasting stability regimes on extreme and fatigue loads on the wind turbine. 
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  3. Climate studies based on global climate models (GCMs) project a steady increase in annual average temperature and severe heat extremes in central North America during the mid-century and beyond. However, the agreement of observed trends with climate model trends varies substantially across the region. The present study focuses on two different locations: Des Moines, IA and Austin, TX. In Des Moines, annual extreme temperatures have not increased over the past three decades unlike the trend of regionally-downscaled GCM data for the Midwest, likely due to a “warming hole” over the area linked to agricultural factors. This warming hole effect is not evident for Austin over the same time period, where extreme temperatures have been higher than projected by regionally-downscaled climate (RDC) forecasts. In consideration of the deviation of such RDC extreme temperature forecasts from observations, this study statistically analyzes RDC data in conjunction with observational data to define for these two cities a 95% prediction interval of heat extreme values by 2040. The statistical model is constructed using a linear combination of RDC ensemble-member annual extreme temperature forecasts with regression coefficients for individual forecasts estimated by optimizing model results against observations over a 52-year training period. 
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  4. Downburst events initialized at various hours during the evening transition (ET) period are simulated to determine the effects of ambient stability on the outflow of downburst winds. The simulations are performed using a pseudo-spectral large eddy simulation model at high resolution to capture both the large-scale flow and turbulence characteristics of downburst winds. First, a simulation of the ET is performed to generate realistic initial and boundary conditions for the subsequent downburst simulations. At each hour in the ET, an ensemble of downburst simulations is initialized separately from the ET simulation in which an elevated cooling source within the model domain generates negatively buoyant air to mimic downburst formation.

    The simulations show that while the stability regime changes, the ensemble mean of the peak wind speed remains fairly constant (between 35 and 38 m s−1) and occurs at the lowest model level for each simulation. However, there is a slight increase in intensity and decrease in the spread of the maximum outflow winds as stability increases as well as an increase in the duration over which these strongest winds persist. This appears to be due to the enhanced maintenance of the ring vortex that results from the low-level temperature inversion, increased ambient shear, and a lack of turbulence within the stable cases. Coherent turbulent kinetic energy and wavelet spectral analysis generally show increased energy in the convective cases and that energy increases across all scales as the downburst passes. 
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