dr. H. Chen (Hao)

I work as an assistant professor in AI and sustainable industries. My current research interest is how to innovatively develop and deploy emerging AI technologies for the unique physical principles and economic mechanisms of sustainable industries, especially for future energy and environment systems, to combat climate change.

I am also passionate about popularizing emerging science and how to advance engineering education. I am proud of my classical Oriental cultural background. I am easygoing and like parties, outdoor activities, playing tennis, classical music, painting, and calligraphy.

Welcome to all PhD, MSc and undergraduate students looking for supervision and collaboration.

Faculty of Behavioural, Management and Social Sciences (BMS), Industrial Engineering & Business Information Systems (IEBIS)

Data-driven study of techno-economic in new industries and optimisation of engineering processes.

Chen, H. (2024). A novel wind model downscaling with statistical regression and forecast for the cleaner energy. Journal of cleaner production, 434, Article 140217. https://doi.org/10.1016/j.jclepro.2023.140217

Chen, H., Birkelund, Y., Ricaud, B., & Zhang, Q. (2023). A southern, middle, and northern Norwegian offshore wind energy resources analysis by a transfer learning method for Energy Internet. Journal of physics: Conference series, 2655(1), Article 012011. https://doi.org/10.1088/1742-6596/2655/1/012011

Chen, H., Birkelund, Y., & Zhang, Q. (2023). Data science in wind energy: A case study for Norwegian offshore wind. Journal of physics: Conference series, 2638(1), Article 012013. https://doi.org/10.1088/1742-6596/2638/1/012013

Chen, H., & Zhang, Q. (2023). A Control Strategy for Electromagnetic Transient in Renewable Energy Systems Based on Virtual Synchronous Generation. In 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) IEEE. https://doi.org/10.1109/ICECCME57830.2023.10252542

Chen, H., & Staupe-Delgado, R. (2022). Exploiting more robust and efficacious deep learning techniques for modeling wind power with speed. Energy Reports, 8, 864-870. https://doi.org/10.1016/j.egyr.2021.11.151

Chen, H., Birkelund, Y., Batalden, B. M., & Barabadi, A. (2022). Noise-intensification data augmented machine learning for day-ahead wind power forecast. Energy Reports, 8(Suppl. 10), 916-922. https://doi.org/10.1016/j.egyr.2022.05.265

Chen, H. (2022). Cluster-based ensemble learning for wind power modeling from meteorological wind data. Renewable and Sustainable Energy Reviews, 167, Article 112652. https://doi.org/10.1016/j.rser.2022.112652

Chen, H., Zhang, Q., & Birkelund, Y. (2022). Machine learning forecasts of Scandinavian numerical weather prediction wind model residuals with control theory for wind energy. Energy Reports, 8(Suppl. 13), 661-668. https://doi.org/10.1016/j.egyr.2022.08.105

Chen, H. (2022). A comprehensive statistical analysis for residuals of wind speed and direction from numerical weather prediction for wind energy. Energy Reports, 8(Suppl. 9), 618-626. https://doi.org/10.1016/j.egyr.2022.07.080

Chen, H., Birkelund, Y., & Yuan, F. (2021). Examination of turbulence impacts on ultra-short-term wind power and speed forecasts with machine learning. Energy Reports, 7, 332-338. https://doi.org/10.1016/j.egyr.2021.08.040

See all research output of this employee

Industrial Engineering Management (IEM) and Busniess Information System (BIS) for undergraduate and masters theses.
Sustainable Supply Chains with D.M. YAZAN.
Applications of Artificial Intelligence in Business with DR. M.R. MACHADO.

Bachelor

Industrial Engineering and Management

Courses in the current academic year are added at the moment they are finalised in the Osiris system. Therefore it is possible that the list is not yet complete for the whole academic year.

201300059 - Internship BIT

+31534897784

h.chen-3@utwente.nl

Visiting Address

University of Twente
Faculty of Behavioural, Management and Social Sciences
Ravelijn (building no. 10), room 4402
Hallenweg 17
7522NH Enschede
The Netherlands

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Mailing Address

University of Twente
Faculty of Behavioural, Management and Social Sciences
Ravelijn 4402
P.O. Box 217
7500 AE Enschede
The Netherlands

Faculty of Behavioural, Management and Social Sciences (BMS), Industrial Engineering & Business Information Systems (IEBIS)

University of Twente Drienerlolaan 5
7522 NB Enschede

+31 (0)53 489 9111
info@utwente.nl
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