Ravelijn

dr. H. Chen (Hao)

Universitair docent
BMS-HBE-IEBIS
Ravelijn 4402
h.chen-3@utwente.nl
+31534897784
Visitekaartje

Ik werk als assistent-professor op het gebied van AI en duurzame industrieën. Mijn huidige onderzoeksinteresse is hoe op innovatieve wijze opkomende AI-technologieën kunnen worden ontwikkeld en ingezet voor de unieke fysische principes en economische mechanismen van duurzame industrieën, met name voor toekomstige energie- en milieusystemen om klimaatverandering tegen te gaan.

Ik ben ook gepassioneerd over het populariseren van opkomende wetenschap en het bevorderen van onderwijs in engineering. Ik ben trots op mijn klassieke Oosterse culturele achtergrond. Ik ben makkelijk in de omgang en hou van feestjes, buitenactiviteiten, tennissen, klassieke muziek, schilderen en kalligrafie.


Expertises

  • Computer Science

    • Models
    • Machine Learning

  • Earth and Planetary Sciences

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

Organisaties

Publicaties

2024

Quantum Computing for Complex Energy Systems: A Review (2024)In 2024 6th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2024 (pp. 572-577). IEEE. Heinen, X. & Chen, H.https://doi.org/10.1109/DOCS63458.2024.10704424Status Quo, Advances and Futures of Machine Learning in Fault Detection and Diagnosis for Energy: A Review (2024)In Proceedings of The 6th International Conference on Clean Energy and Electrical Systems - Proceedings of CEES 2024 (pp. 170-183) (Lecture Notes in Electrical Engineering; Vol. 1222 LNEE). Springer. Chen, H., Feng, J., Jin, A. & Li, B.https://doi.org/10.1007/978-981-97-5775-6_12Cost-Efficient Vehicular Edge Computing Deployment for Mobile Air Pollution Monitoring (2024)In 2024 IEEE Wireless Communications and Networking Conference, WCNC 2024 - Proceedings (IEEE Wireless Communications and Networking Conference, WCNC). IEEE. Zhang, Q., Chen, H. & Ha, P. H.https://doi.org/10.1109/WCNC57260.2024.10570558A novel wind model downscaling with statistical regression and forecast for the cleaner energy (2024)Journal of cleaner production, 434. Article 140217. Chen, H.https://doi.org/10.1016/j.jclepro.2023.140217Scenario Generation by Physical Model-Free Approaches for Multiple Renewables (2024)Journal of physics: Conference series, 2754(1). Article 012003. Chen, H., Jin, A., Zhao, W., Yi, H. & Zhang, Q.https://doi.org/10.1088/1742-6596/2754/1/012003

2023

A Control Strategy for Electromagnetic Transient in Renewable Energy Systems Based on Virtual Synchronous Generation (2023)In 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME). IEEE. Chen, H. & Zhang, Q.https://doi.org/10.1109/ICECCME57830.2023.10252542A southern, middle, and northern Norwegian offshore wind energy resources analysis by a transfer learning method for Energy Internet (2023)Journal of physics: Conference series, 2655(1). Article 012011. Chen, H., Birkelund, Y., Ricaud, B. & Zhang, Q.https://doi.org/10.1088/1742-6596/2655/1/012011Data science in wind energy: A case study for Norwegian offshore wind (2023)Journal of physics: Conference series, 2638(1). Article 012013. Chen, H., Birkelund, Y. & Zhang, Q.https://doi.org/10.1088/1742-6596/2638/1/012013

2022

A comprehensive statistical analysis for residuals of wind speed and direction from numerical weather prediction for wind energy (2022)Energy Reports, 8(Suppl. 9), 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 (2022)Energy Reports, 8(Suppl. 13), 661-668. Chen, H., Zhang, Q. & Birkelund, Y.https://doi.org/10.1016/j.egyr.2022.08.105

Onderzoeksprofielen

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