Jinghao Wang
About
I hold a Bachelor of Science degree in Safety Engineering from the China University of Mining and Technology, and a Master of Science degree in Reliability, Availability, Maintainability, and Safety from the Norwegian University of Science and Technology. My current research focus is associated with the Inthydro project, encompassing AI-assisted long-term hydropower scheduling, electricity market dynamics, power system balancing, and machine learning applications.
The anticipated outcomes of my research are as follows:
- Enhancement of the current model used in hydropower scheduling. The scheduling methodology under development could offer a novel approach to hydropower scheduling, with machine learning and AI techniques potentially providing a superior alternative.
- Development of a new scheduling tool to aid in the dispatch of renewable energy. The objective is to make the scheduling process more flexible and accurate, not only for short-term but also for long-term scheduling.
- Integration of the scheduling model with other estimation and simulation models. Given that numerous factors influence hydropower scheduling, merging multiple simulation methods into one model can be overly complex and unreliable. As such, we aim to devise a solution for the effective linkage of different simulation tools.
Competencies
Publications
2024
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Wang, Jinghao;
Yousefi, Mojtaba;
Rajasekharan, Jayaprakash;
Arghandeh, Reza;
Farahmand, Hossein.
(2024)
Exploring the application of machine-learning techniques in the next generation of long-term hydropower-thermal scheduling.
IET Renewable Power Generation
Academic article
2023
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Yousefi, Mojtaba;
Wang, Jinghao;
Høivik, Øivind Fandrem;
Rajasekharan, Jayaprakash;
Wierling, August Hubert;
Farahmand, Hossein.
(2023)
Short-term inflow forecasting in a dam-regulated river in Southwest Norway using causal variational mode decomposition.
Scientific Reports
Academic article
2022
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XU, CHENG;
Zhao, Meng;
Zhang, Jianhua;
Wang, Jinghao;
Pan, Xueping;
Liu, Xiufeng.
(2022)
TransNILM: A Transformer-based Deep Learning Model for Non-intrusive Load Monitoring.
IEEE Xplore Digital Library
Academic article
Journal publications
-
Wang, Jinghao;
Yousefi, Mojtaba;
Rajasekharan, Jayaprakash;
Arghandeh, Reza;
Farahmand, Hossein.
(2024)
Exploring the application of machine-learning techniques in the next generation of long-term hydropower-thermal scheduling.
IET Renewable Power Generation
Academic article
-
Yousefi, Mojtaba;
Wang, Jinghao;
Høivik, Øivind Fandrem;
Rajasekharan, Jayaprakash;
Wierling, August Hubert;
Farahmand, Hossein.
(2023)
Short-term inflow forecasting in a dam-regulated river in Southwest Norway using causal variational mode decomposition.
Scientific Reports
Academic article
-
XU, CHENG;
Zhao, Meng;
Zhang, Jianhua;
Wang, Jinghao;
Pan, Xueping;
Liu, Xiufeng.
(2022)
TransNILM: A Transformer-based Deep Learning Model for Non-intrusive Load Monitoring.
IEEE Xplore Digital Library
Academic article
Outreach
2022
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LectureWang, Jinghao. (2022) Machine learning-based forepart block decomposition model as a precursor to the Scenario fan problem. NTNU, SINTEF, StatKraft International Conference on Hydropower Scheduling in Competitive Electricity markets , Statkraft, Oslo, Norway, 2022-09-12 - 2022-09-15
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Academic lectureWang, Jinghao; Yousefi, Mojtaba; Cheng, Xiaomei; Rajasekharan, Jayaprakash; Arghandeh, Reza; Pan, Xueping. (2022) Self-organizing maps for scenario reduction in long-term hydropower scheduling. IEEE IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society , Brussels 2022-10-17 - 2022-10-20