Dr BU Siqi, Member of the Otto Poon Charitable Foundation Research Institute for Smart Energy (RISE), Associate Professor and Associate Head of the Department of Electrical and Electronic Engineering, and his teammates developed a novel deep reinforcement learning-based interpretable framework considering outliers for the accurate prediction of photovoltaic (PV) power. The study has recently been published in the journal Sustainable Energy Technologies and Assessments.
The team utilised cutting-edge deep reinforcement learning-based interpretable models for predicting the occurrence of PV power outliers and improving PV power prediction accuracy. The proposed framework not only distinguishes outliers in forecast data, but also obtains complete outlier information with correct classification of PV power types, which can help engineers take emergency measures such as participating in demand response, increasing energy reserves, and optimising bids.
The framework was verified on the actual operation data of the PV plant in Northwest China. The prediction performance of the proposed framework was analysed and compared with benchmark methods. Research results demonstrated that the proposed PV power prediction framework has great potential for application in future electric energy systems.
Read the full article: https://www.sciencedirect.com/science/article/pii/S2213138824002261?dgcid=coauthor#appSB
| Research Units | Otto Poon Charitable Foundation Research Institute for Smart Energy |
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