TY - GEN
T1 - Assessing ANN Architectures for Wind Turbine Power Prediction
T2 - 2023 International Conference on Decision Aid Sciences and Applications, DASA 2023
AU - Achouri, Fethi
AU - Damou, Mehdi
AU - Harrou, Fouzi
AU - Sun, Ying
AU - Bouyeddou, Benamar
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - With the increasing importance of renewable energy sources, wind power has emerged as a significant contributor to the global energy mix. Accurate prediction of wind power production is essential for efficient grid integration and optimal utilization of wind resources. This study presents an investigation into the performance of various artificial neural network (ANN) models for wind power prediction. Different ANN architectures, including narrow, medium, and wide networks, as well as bilayered and trilayered structures, were explored to understand their impact on predictive capabilities. The data used for evaluation was collected from a 2.05 MW Senvion MM82 wind turbine. The performance of the ANN models was compared with Linear Regression (LR), Interactions LR, Robust LR, and Stepwise LR methods. Results revealed that the bilayered and trilayered ANN s achieved the best performance in wind power prediction. This study highlights the potential of ANN models in accurately predicting wind power, thereby facilitating efficient and reliable wind farm operations.
AB - With the increasing importance of renewable energy sources, wind power has emerged as a significant contributor to the global energy mix. Accurate prediction of wind power production is essential for efficient grid integration and optimal utilization of wind resources. This study presents an investigation into the performance of various artificial neural network (ANN) models for wind power prediction. Different ANN architectures, including narrow, medium, and wide networks, as well as bilayered and trilayered structures, were explored to understand their impact on predictive capabilities. The data used for evaluation was collected from a 2.05 MW Senvion MM82 wind turbine. The performance of the ANN models was compared with Linear Regression (LR), Interactions LR, Robust LR, and Stepwise LR methods. Results revealed that the bilayered and trilayered ANN s achieved the best performance in wind power prediction. This study highlights the potential of ANN models in accurately predicting wind power, thereby facilitating efficient and reliable wind farm operations.
KW - artificial neural networks
KW - power prediction
KW - regression learning
KW - Wind turbines
UR - http://www.scopus.com/inward/record.url?scp=85177469114&partnerID=8YFLogxK
U2 - 10.1109/DASA59624.2023.10286676
DO - 10.1109/DASA59624.2023.10286676
M3 - Conference contribution
AN - SCOPUS:85177469114
T3 - 2023 International Conference on Decision Aid Sciences and Applications, DASA 2023
SP - 284
EP - 289
BT - 2023 International Conference on Decision Aid Sciences and Applications, DASA 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 16 September 2023 through 17 September 2023
ER -
