| 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 | 86 | 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 |
| Machine learning for applied weather prediction SE Haupt, J Cowie, S Linden, T McCandless, B Kosovic, S Alessandrini 2018 IEEE 14th international conference on e-science (e-Science), 276-277, 2018 | 69 | 2018 |
| An objective methodology for configuring and down-selecting an NWP ensemble for low-level wind prediction JA Lee, WC Kolczynski, TC McCandless, SE Haupt Monthly Weather Review 140 (7), 2270-2286, 2012 | 58 | 2012 |
| A model tree approach to forecasting solar irradiance variability TC McCandless, SE Haupt, GS Young Solar Energy 120, 514-524, 2015 | 54 | 2015 |
| Solar irradiance nowcasting case studies near Sacramento JA Lee, SE Haupt, PA Jiménez, MA Rogers, SD Miller, TC McCandless Journal of Applied Meteorology and Climatology 56 (1), 85-108, 2017 | 47 | 2017 |
| Combining artificial intelligence with physics-based methods for probabilistic renewable energy forecasting SE Haupt, TC McCandless, S Dettling, S Alessandrini, JA Lee, S Linden, ... Energies 13 (8), 1979, 2020 | 45 | 2020 |
| The SunCast solar-power forecasting system: the results of the public-private-academic partnership to advance solar power forecasting SE Haupt, B Kosovic, T Jensen, J Lee, P Jimenez, J Lazo, J Cowie, ... National Center for Atmospheric Research (NCAR), Boulder (CO): Research …, 2016 | 40 | 2016 |
| Regime-dependent short-range solar irradiance forecasting TC McCandless, GS Young, SE Haupt, LM Hinkelman Journal of Applied Meteorology and Climatology 55 (7), 1599-1613, 2016 | 35 | 2016 |
| Blending distributed photovoltaic and demand load forecasts SE Haupt, S Dettling, JK Williams, J Pearson, T Jensen, T Brummet, ... Solar Energy 157, 542-551, 2017 | 32 | 2017 |
| Enhancing wildfire spread modelling by building a gridded fuel moisture content product with machine learning TC McCandless, B Kosovic, W Petzke Machine Learning: Science and Technology 1 (3), 035010, 2020 | 24 | 2020 |
| Comparison of implicit vs. explicit regime identification in machine learning methods for solar irradiance prediction T McCandless, S Dettling, SE Haupt Energies 13 (3), 689, 2020 | 21 | 2020 |
| Examining the potential of a random forest derived cloud mask from GOES-R satellites to improve solar irradiance forecasting T McCandless, PA Jiménez Energies 13 (7), 1671, 2020 | 20 | 2020 |
| The schaake shuffle technique to combine solar and wind power probabilistic forecasting S Alessandrini, T McCandless Energies 13 (10), 2503, 2020 | 19 | 2020 |
| Machine learning for improving surface-layer-flux estimates T McCandless, DJ Gagne, B Kosović, SE Haupt, B Yang, C Becker, ... Boundary-Layer Meteorology 185 (2), 199-228, 2022 | 17 | 2022 |
| Climatology of wind variability for the Shagaya region in Kuwait SM Naegele, TC McCandless, SJ Greybush, GS Young, SE Haupt, ... Renewable and Sustainable Energy Reviews 133, 110089, 2020 | 16 | 2020 |
| The Effects of Imputing Missing Data on Ensemble Temperature Forecasts. TC McCandless, SE Haupt, GS Young J. Comput. 6 (2), 162-171, 2011 | 16 | 2011 |
| The super-turbine wind power conversion paradox: using machine learning to reduce errors caused by Jensen's inequality TC McCandless, SE Haupt Wind Energy Science 4 (2), 343-353, 2019 | 12 | 2019 |
| Short term solar radiation forecasts using weather regime-dependent artificial intelligence techniques TC McCandless, SE Haupt, GS Young Proceedings of the 12th Conference on Artificial and Computational …, 2014 | 11 | 2014 |
| Machine learning parameterization of the surface layer: bridging the observation-modeling gap DJ Gagne, T McCandless, B Kosovic, A DeCastro, R Loft, SE Haupt, ... AGU Fall Meeting Abstracts 2019, IN44A-04, 2019 | 5 | 2019 |
