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Confirmation of Candidature -Candidate : Ananta Neupane

Development and Comparison of Hybrid Deep Learning Artificial Intelligent Models for Wind Speed Forecasting in Wind-Rich Regions in Australia.
25 OCT 2021
10.00 AM - 11.30 AM

The unpredictable nature of wind is a major challenge for wind speed forecasting and Hybrid Deep Learning Artificial Intelligent Models can provide highly accurate forecasting, which are not widely available in the Australian context. More specifically, the study will firstly decompose wind data using RLMD techniques and EMD and its variants, then the LSTM model will be used to predict wind speeds. The study will use moDWT and VMD technique to decompose wind data and predict wind speed using the LSTMT hybrid model. In addition to this, wind data is also decomposed by MEMD techniques with feature selection to build a BiLSTM hybrid model to predict wind speed to have a comprehensive comparison with benchmark models.

For more information, please contact the Graduate Research School.