Sue Ellen Haupt
Citat de
Citat de
Practical genetic algorithms
RL Haupt, SE Haupt
John Wiley & Sons, 2004
Using artificial intelligence to improve real-time decision-making for high-impact weather
A McGovern, KL Elmore, DJ Gagne, SE Haupt, CD Karstens, R Lagerquist, ...
Bulletin of the American Meteorological Society 98 (10), 2073-2090, 2017
WRF-Solar: Description and clear-sky assessment of an augmented NWP model for solar power prediction
PA Jimenez, JP Hacker, J Dudhia, SE Haupt, JA Ruiz-Arias, ...
Bulletin of the American Meteorological Society 97 (7), 1249-1264, 2016
Solar forecasting: methods, challenges, and performance
A Tuohy, J Zack, SE Haupt, J Sharp, M Ahlstrom, S Dise, E Grimit, ...
IEEE Power and Energy Magazine 13 (6), 50-59, 2015
Artificial intelligence methods in the environmental sciences
SE Haupt, A Pasini, C Marzban
Springer Science & Business Media, 2008
Storm-based probabilistic hail forecasting with machine learning applied to convection-allowing ensembles
DJ Gagne, A McGovern, SE Haupt, RA Sobash, JK Williams, M Xue
Weather and forecasting 32 (5), 1819-1840, 2017
Interpretable deep learning for spatial analysis of severe hailstorms
DJ Gagne II, SE Haupt, DW Nychka, G Thompson
Monthly Weather Review 147 (8), 2827-2845, 2019
A wind power forecasting system to optimize grid integration
WP Mahoney, K Parks, G Wiener, Y Liu, WL Myers, J Sun, ...
IEEE Transactions on Sustainable Energy 3 (4), 670-682, 2012
Short-term wind forecast of a data assimilation/weather forecasting system with wind turbine anemometer measurement assimilation
WYY Cheng, Y Liu, AJ Bourgeois, Y Wu, SE Haupt
Renewable Energy 107, 340-351, 2017
Improving pollutant source characterization by better estimating wind direction with a genetic algorithm
CT Allen, GS Young, SE Haupt
Atmospheric Environment 41 (11), 2283-2289, 2007
A review of the potential impacts of climate change on bulk power system planning and operations in the United States
MT Craig, S Cohen, J Macknick, C Draxl, OJ Guerra, M Sengupta, ...
Renewable and Sustainable Energy Reviews 98, 255-267, 2018
Recent trends in variable generation forecasting and its value to the power system
KD Orwig, ML Ahlstrom, V Banunarayanan, J Sharp, JM Wilczak, ...
IEEE Transactions on Sustainable Energy 6 (3), 924-933, 2014
A demonstration of coupled receptor/dispersion modeling with a genetic algorithm
SE Haupt
Atmospheric Environment 39 (37), 7181-7189, 2005
A preliminary study of assimilating numerical weather prediction data into computational fluid dynamics models for wind prediction
FJ Zajaczkowski, SE Haupt, KJ Schmehl
Journal of Wind Engineering and Industrial Aerodynamics 99 (4), 320-329, 2011
Validation of a receptor–dispersion model coupled with a genetic algorithm using synthetic data
SE Haupt, GS Young, CT Allen
Journal of applied meteorology and climatology 45 (3), 476-490, 2006
Source characterization with a genetic algorithm–coupled dispersion–backward model incorporating SCIPUFF
CT Allen, SE Haupt, GS Young
Journal of applied meteorology and climatology 46 (3), 273-287, 2007
Variable generation power forecasting as a big data problem
SE Haupt, B Kosović
IEEE Transactions on Sustainable Energy 8 (2), 725-732, 2016
A regime-dependent artificial neural network technique for short-range solar irradiance forecasting
TC McCandless, SE Haupt, GS Young
Renewable Energy 89, 351-359, 2016
Building the Sun4Cast system: Improvements in solar power forecasting
SE Haupt, B Kosović, T Jensen, JK Lazo, JA Lee, PA Jiménez, J Cowie, ...
Bulletin of the American Meteorological Society 99 (1), 121-136, 2018
On bridging a modeling scale gap: Mesoscale to microscale coupling for wind energy
SE Haupt, B Kosovic, W Shaw, LK Berg, M Churchfield, J Cline, C Draxl, ...
Bulletin of the American Meteorological Society 100 (12), 2533-2550, 2019
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