Dirección de Investigación
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Jorge Ángel González Ordiano

SNI I

  • INIAT

Soy ingeniero mecánico egresado del Karlsruhe Institute of Technology en Alemania, donde también completé una maestría (2016) y un doctorado (2019). Mi tesis doctoral estuvo enfocada en el uso de la ciencia de datos dentro del contexto de la transición energética. Esta investigación formó parte del proyecto Energy System 2050 de la Asociación Helmholtz. Al finalizar mi doctorado, me incorporé como investigador posdoctoral a la Colorado State University dentro del grupo de Steve Simske. Durante este periodo me dediqué a desarrollar métodos para combatir el comercio ilícito mediante el uso de la ciencia de datos. En noviembre del 2020 me uní a la Universidad Iberoamericana Ciudad de México como académico de tiempo completo.

I received my Bachelor’s (2014) and Master’s (2016) degrees in Mechanical Engineering from the Karlsruhe Institute of Technology (KIT) in Germany, where I also completed my PhD (2019). My PhD research focused on using data science to aid the energy transition and was part of the Helmholtz Association's Energy System 2050 project. After finishing my PhD, I joined the Colorado State University as a postdoctoral research fellow, where I worked within the lab of Professor Steve Simske. During this period, my research consisted in developing methods to combat illicit trade using data science. In November of 2020, I joined the Universidad Iberoamericana Ciudad de México as a full-time professor.

 

Publicaciones

Portada Tipo de publicación Descripción
Artículo

González Ordiano, J. Á., Bartschat, A., Ludwig, N., Braun, E., Waczowicz, S., Renkamp, N., … & Hagenmeyer, V. (2018). Concept and Benchmark Results for Big Data Energy Forecasting based on Apache Spark. Journal of Big Data, 5(1), 11.
Artículo

Waczowicz, S., Ludwig, N., González Ordiano, J. Á., Mikut, R., & Hagenmeyer, V. (2018, June). Demand Response Clustering: Automatically Finding Optimal Cluster Hyper-Parameter Values. In Proceedings of the Ninth International Conference on Future Energy Systems (pp. 429-430). ACM.
Artículo

González Ordiano, J.Á., Finn, L., Winterlich, A., Moloney, G., & Simske, S. (2020, June). A Method for Estimating Driving Factors of Illicit Trade Using Node Embeddings and Clustering. In Proceedings of the 12th Mexican Conference on Pattern Recognition.
Artículo

González Ordiano, J.Á., Finn, L., Winterlich, A., Moloney, G., & Simske, S. (2020, June). On the Analysis of Illicit Supply Networks using Variable State Resolution-Markov Chains. In Proceedings of the 18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems.
Artículo

González Ordiano, J. Á., Gröll, L., Mikut, R., & Hagenmeyer, V. (2020). Probabilistic Energy Forecasting using the Nearest Neighbors Quantile Filter and Quantile Regression. International Journal of Forecasting, 36(2), 310-323.
Artículo

Appino, R. R., González Ordiano, J. A., Munzke, N., Mikut, R., Faulwasser, T., & Hagenmeyer, V. (2019, May). Assessment of a scheduling strategy for dispatching prosumption of an industrial campus. In International ETG-Congress 2019; ETG Symposium (pp. 1-6). VDE.
Artículo

Sharma, V., Cali, U., Hagenmeyer, V., Mikut, R., & González Ordiano, J. Á. (2018, June). Numerical Weather Prediction Data Free Solar Power Forecasting with Neural Networks. In Proceedings of the Ninth International Conference on Future Energy Systems (pp. 604-609). ACM.
Artículo

Appino, R. R., González Ordiano, J. Á., Mikut, R., Hagenmeyer, V., & Faulwasser, T. (2018, June). Storage Scheduling with Stochastic Uncertainties: Feasibility and Cost of Imbalances. In Proceedings of the 2018 Power Systems Computation Conference (PSCC) (pp. 1-7). IEEE.
Artículo

González Ordiano, J. Á., Waczowicz, S., Hagenmeyer, V., & Mikut, R. (2018). Energy Forecasting Tools and Services. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(2), e1235.
Artículo

Appino, R. R., Muñoz-Ortiz, M., González Ordiano, J. Á., Mikut, R., Hagenmeyer, V., & Faulwasser, T. (2019). Reliable Dispatch of Renewable Generation via Charging of Time-Varying PEV Populations. IEEE Transactions on Power Systems, 34(2), 1558-1568.
Artículo

Appino, R. R., González Ordiano, J. Á., Mikut, R., Faulwasser, T., & Hagenmeyer, V. (2018). On the Use of Probabilistic Forecasts in Scheduling of Renewable Energy Sources Coupled to Storages. Applied Energy, 210, 1207-1218.
Artículo

Jakob, W., González Ordiano, J. Á., Ludwig, N., Mikut, R., & Hagenmeyer, V. (2017, July). Towards Coding Strategies for Forecasting-based Scheduling in Smart Grids and the Energy Lab 2.0. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 1271-1278). ACM.
Artículo

González Ordiano, J. Á., Waczowicz, S., Reischl, M., Mikut, R., & Hagenmeyer, V. (2017). Photovoltaic Power Forecasting using Simple Data-Driven Models without Weather Data. Computer Science-Research and Development, 32(1-2), 237-246.
Artículo

Popova, A. A., Depew, C., Permana, K. M., Trubitsyn, A., Peravali, R., González Ordiano, J. Á., … & Levkin, P. A. (2017). Evaluation of the Droplet-Microarray Platform for High-Throughput Screening of Suspension Cells. SLAS TECHNOLOGY: Translating Life Sciences Innovation, 22(2), 163-175.
Artículo

González Ordiano, J. Á., Doneit, W., Waczowicz, S., Gröll, L., Mikut, R., & Hagenmeyer, V. (2017, November). Nearest-Neighbor based Non-Parametric Probabilistic Forecasting with Applications in Photovoltaic Systems. In Proceedings of the 26. Workshop Computational Intelligence (pp. 9-30).
Artículo

Appino, R. R., Wang, H., González Ordiano, J. Á., Faulwasser, T., Mikut, R., Hagenmeyer, V., & Mancarella, P. (2021). Energy-based stochastic mpc for integrated electricity-hydrogen vpp in real-time markets. Electric Power Systems Research, 195, 106738. 
Artículo

González-Ordiano, J. Á., Mühlpfordt, T., Braun, E., Liu, J., Çakmak, H., Kühnapfel, U., Düpmeier, C., Waczowicz, S., Faulwasser, T., Mikut, R., Hagenmeyer, V., & Appino, R. R. (2021). Probabilistic forecasts of the distribution grid state using data-driven forecasts and probabilistic power flow. Applied Energy, 302, 117498.