dr. H. Chen (Hao)

Assistant Professor
BMS-HBE-IEBIS
Ravelijn 4402
h.chen-3@utwente.nl
+31534897784
Business card

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.

Expertise

  • Computer Science

    • Models
    • Machine Learning

  • Earth and Planetary Sciences

    • Model
    • Prediction
    • Wind
    • Wind Velocity
    • Wind Power
    • Weather Condition

Organisations

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

Publications

2024
A novel wind model downscaling with statistical regression and forecast for the cleaner energy, Article 140217. Chen, H.https://doi.org/10.1016/j.jclepro.2023.140217
2023
A Control Strategy for Electromagnetic Transient in Renewable Energy Systems Based on Virtual Synchronous GenerationIn 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME). IEEE. Chen, H. & Zhang, Q.https://doi.org/10.1109/ICECCME57830.2023.10252542Data science in wind energy: A case study for Norwegian offshore wind, Article 012013. Chen, H., Birkelund, Y. & Zhang, Q.https://doi.org/10.1088/1742-6596/2638/1/012013A southern, middle, and northern Norwegian offshore wind energy resources analysis by a transfer learning method for Energy Internet, Article 012011. Chen, H., Birkelund, Y., Ricaud, B. & Zhang, Q.https://doi.org/10.1088/1742-6596/2655/1/012011
2022
A comprehensive statistical analysis for residuals of wind speed and direction from numerical weather prediction for wind energy, 618-626. Chen, H.https://doi.org/10.1016/j.egyr.2022.07.080Machine learning forecasts of Scandinavian numerical weather prediction wind model residuals with control theory for wind energy, 661-668. Chen, H., Zhang, Q. & Birkelund, Y.https://doi.org/10.1016/j.egyr.2022.08.105Noise-intensification data augmented machine learning for day-ahead wind power forecast, 916-922. Chen, H., Birkelund, Y., Batalden, B. M. & Barabadi, A.https://doi.org/10.1016/j.egyr.2022.05.265Cluster-based ensemble learning for wind power modeling from meteorological wind data, Article 112652. Chen, H.https://doi.org/10.1016/j.rser.2022.112652Exploiting more robust and efficacious deep learning techniques for modeling wind power with speed, 864-870. Chen, H. & Staupe-Delgado, R.https://doi.org/10.1016/j.egyr.2021.11.151
2021
An Evaluation on Diverse Machine Learning Algorithms for Hourly Univariate Wind Power Prediction in the Arctic, Article 012016. Chen, H. & Birkelund, Y.https://doi.org/10.1088/1742-6596/2141/1/012016

Research profiles

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

Affiliated study programs

Courses academic year 2023/2024

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.

Address

University of Twente

Ravelijn (building no. 10), room 4402
Hallenweg 17
7522 NH Enschede
Netherlands

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Organisations

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