Research output per year
Research output per year
Project Details
Description
This project aims to design and develop light-AI, including computation-light AI and data-light AI, to enable cognitive power electronics, which holds great potential to address longstanding challenges in operation optimization and condition monitoring of power electronics (PE).
Layman's description
Renewable energy generation and electrification are among the top priorities all over the world. An essential part of renewable energy generation is power electronics (PE) that perform electrical energy conversion. For example, between a wind turbine and the electric grid, a power electronic converter is necessary to provide efficient and reliable grid integration due to the differences in voltage and frequency.
In this project, researchers from the Department of Energy Technology and Department of Computer Science at Aalborg University will combine AI and energy research to design and develop power electronics that can adapt to dynamic operation settings and predict failures.
In this project, researchers from the Department of Energy Technology and Department of Computer Science at Aalborg University will combine AI and energy research to design and develop power electronics that can adapt to dynamic operation settings and predict failures.
| Status | Finished |
|---|---|
| Effective start/end date | 01/08/2019 → 31/07/2023 |
Collaborative partners
Funding
- Villum Foundation: DKK2,996,278.00
UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):
Fingerprint
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Research output
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AutoCTS++: Zero-shot Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecasting
Wu, X., Wu, X., Yang, B., Zhou, L., Guo, C., Qiu, X., Hu, J., Sheng, Z. & Jensen, C. S., Sept 2024, In: The VLDB Journal. 33, 5, p. 1743-1770 28 p., 5.Research output: Contribution to journal › Journal article › Research › peer-review
6 Citations (Scopus)293 Downloads (Pure) -
QCore: Data-Effiicient, On-Device Continual Calibration for Quantized Models
Campos, D., Yang, B., Kieu, T., Zhang, M., Guo, C. & Jensen, C. S., 2024, In: Proceedings of the VLDB Endowment. 17, 11, p. 2708-2721 14 p., 11.Research output: Contribution to journal › Conference article in Journal › Research › peer-review
Open AccessFile6 Citations (Scopus)44 Downloads (Pure) -
AutoCTS+: Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecasting.
Wu, X., Zhang, D., Zhang, M., Guo, C., Yang, B. & Jensen, C. S., 2023, In: Proceedings of the ACM on Management of Data. 1, 1, p. 97:1-97:26Research output: Contribution to journal › Conference article in Journal › Research › peer-review
Press/Media
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Villum Fonden støtter otte projekter med i alt 52,5 mio. kr.
Larsen, K. G., Rasmussen, M. R., Hose, K., Albertsen, M., Wang, H. & Yang, B.
07/05/2020
1 item of Media coverage
Press/Media: Press / Media
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Villum Fonden støtter 8 tværfaglige projekter i nyt program
Hose, K., Albertsen, M., Wang, H., Yang, B., Larsen, K. G. & Rasmussen, M. R.
29/03/2020
1 item of Media coverage
Press/Media: Press / Media