Doubleday, K., Van Scyoc Hernandez, V., and Hodge, B.-M. (2020). Benchmark probabilistic solar forecasts: Characteristics and recommendations. Solar Energy, 206:52-67.
Driemel, A., Augustine, J., Behrens, K., Colle, S., Cox, C., Cuevas-Agulló, E., Denn, F. M., Duprat, T., Fukuda, M., Grobe, H., Haeffelin, M., Hodges, G., Hyett, N., Ijima, O., Kallis, A., Knap, W., Kustov, V., Long, C. N., Longenecker, D., Lupi, A., Maturilli, M., Mimouni, M., Ntsangwane, L., Ogihara, H., Olano, X., Olefs, M., Omori, M., Passamani, L., Pereira, E. B., Schmithüsen, H., Schumacher, S., Sieger, R., Tamlyn, J., Vogt, R., Vuilleumier, L., Xia, X., Ohmura, A., and König-Langlo, G. (2018). Baseline surface radiation network (bsrn): structure and data description (1992-2017). Earth System Science Data, 10(3):1491-1501.
Equer, L., Rusch, T. K., and Mishra, S. (2023). Multi-scale message passing neural pde solvers. ARXIV.ORG.
Feichtenhofer, C., Li, Y., He, K., et al. (2022). Masked autoencoders as spatiotemporal learners. Advances in neural information processing systems, 35:35946-35958.
Haeffelin, M. (2014). Basic measurements of radiation at station Palaiseau (2009-02). Backup Publisher: Laboratoire de Méteorologie Dynamique du C.N.R.S., Ecole Polytechnique Type: data set.
Hendrycks, D. and Gimpel, K. (2016). Gaussian error linear units (gelus). arXiv preprint arXiv:1606.08415.
Holmgren, W., Calama-Consulting, Hansen, C., Mikofski, M., Lorenzo, T., Krien, U., bmu, Anderson, K., Stark, C., DaCoEx, Driesse, A., konstant_t, mayudong, de León Peque, M. S., Heliolytics, Miller, E., Anoma, M. A., Boeman, L., jforbess, tylunel, Guo, V., Morgan, A., Stein, J., Leroy, C., R, A. M., JPalakapillyKWH, Dollinger, J., Anderson, K., MLEEFS, and Dowson, O. (2020). pvlib/pvlib-python: v0.8.0.
Holmlund, K. (2003). Eumetsat current and future plans on product generation and dissemination.
Ineichen, P. (2016). Validation of models that estimate the clear sky global and beam solar irradiance. Solar Energy, 132:332-344.
Jacob, B., Peter, D., and Jack, K. (2022). Ocf datapipes. https://github.com/openclimatefix/ocf datapipes.
Jiang, C. M., Esmaeilzadeh, S., Azizzadenesheli, K., Kashinath, K., Mustafa, M., Tchelepi, H. A., Marcus, P., Prabhat, and Anandkumar, A. (2020). Meshfreeflownet: A physics-constrained deep continuous space-time super-resolution framework. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC '20. IEEE Press.
Jönner, J. F., Brunø Lottrup, F., Berg, B., Zhang, D., and Chen, K. (2023). Probabilistic forecasts of global horizontal irradiance for solar systems. IEEE Sensors Letters, 7(1):1-4.
Kitaev, N., Kaiser, L., and Levskaya, A. (2020). Reformer: The efficient transformer. In International Conference on Learning Representations.
Knap, W. (2007). Horizon at station Cabauw. Backup Publisher: Koninklijk Nederlands Meteorologisch Instituut, De Bilt Type: data set.
Koenker, R. and Hallock, K. F. (2001). Quantile regression. Journal of economic perspectives, 15(4):143-156.
Kovachki, N., Li, Z., Liu, B., Azizzadenesheli, K., Bhattacharya, K., Stuart, A., and Anandkumar, A. (2021). Neural operator: Learning maps between function spaces.
Lakshminarayanan, B., Pritzel, A., and Blundell, C. (2016). Simple and scalable predictive uncertainty estimation using deep ensembles. NIPS.
Li, Z., Kovachki, N., Azizzadenesheli, K., Liu, B., Stuart, A., Bhattacharya, K., and Anandkumar, A. (2020). Multipole graph neural operator for parametric partial differential equations. In Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., and Lin, H., editors, Advances in Neural Information Processing Systems, volume 33, pages 6755-6766. Curran Associates, Inc.