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机译:基于深度学习的短期风速预测演化模型 - 以卢尔德鲁德海上风电场为例
Optimisation and Logistics Group School of Computer Science University of Adelaide Australia;
Department of Astronautics Electrical and Energy Engineering (DIAEE) Sapienza University of Rome Italy;
The Australian Institute for Machine Learning The University of Adelaide Adelaide Australia;
Center for Artificial Intelligence Research and Optimization Torrens University Australia Brisbane QLD 4006 Australia|Yonsei Frontier Lab Yonsei University Seoul Republic of Korea;
School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden;
Department of Planning Design and Technology of Architecture Sapienza University of Rome Italy;
Optimisation and Logistics Group School of Computer Science University of Adelaide Australia;
Optimisation and Logistics Group School of Computer Science University of Adelaide Australia;
Deep learning models; Evolutionary algorithms; Generalised normal distribution optimisation; Hybrid evolutionary deep learning method; Short-term forecasting; Wind speed prediction;
机译:使用Lillgrund和Horns Rev-I海上风电场的转子有效风速估算湍流强度
机译:进化产品单元神经网络用于风电场的短期风速预测
机译:新疆风电场短期风速预测对抗对外自动编码器的消失时力矩的进化依赖性多目标集合模型
机译:基于Arima模型的风电场风速短期预测
机译:基于BP和Adaboost_BP的风速和风能的短期预测。
机译:海上风电场(OWF)对常见的海岸蟹Carcinus maenas的影响:Lillgrund海上风电场(瑞典)的标记试验
机译:利用Lillgrund和Horns Rev-I海上风电场的转子有效风速估算湍流强度