Practical genetic algorithms RL Haupt, SE Haupt John Wiley & Sons, 2004 | 8486 | 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 | 405 | 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 | 257 | 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 | 216 | 2015 |
Artificial intelligence methods in the environmental sciences SE Haupt, A Pasini, C Marzban Springer Science & Business Media, 2008 | 190 | 2008 |
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 | 178 | 2019 |
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 | 178 | 2017 |
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 | 178 | 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 | 147 | 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 | 119 | 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 | 111 | 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 | 108 | 2014 |
A demonstration of coupled receptor/dispersion modeling with a genetic algorithm SE Haupt Atmospheric Environment 39 (37), 7181-7189, 2005 | 104 | 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 | 102 | 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 | 90 | 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 | 88 | 2007 |
Variable generation power forecasting as a big data problem SE Haupt, B Kosović IEEE Transactions on Sustainable Energy 8 (2), 725-732, 2016 | 85 | 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 | 84 | 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 | 83 | 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 | 72 | 2019 |