基于数据驱动模式的波浪能装置短期发电功率预测方法

 分析了随着波浪能发电技术的逐步成熟带来的功率预测技术现状，阐述了功率预测对规模化利用波浪能的现实需求，研究了不同模型的预测机理和特性，并在传统物理模型技术上提出了基于深度学习的数据驱动模型。基于长短时记忆网络的深度模型能够对波浪发电装置的短期功率开展预测，并通过与支持向量机、神经网络等模型的比较，证明了长短时记忆网络模型预测方法能够获得更优的短期预测结果。

Short term prediction of ocean wave energy power using long-short term memory network

The prediction technologies of the power generation from the wave energy converters (WEC) are an urgent and crucial problem in the renewable energy planning, the power grid dispatching and the economic operation. Besides the statistical modelling, this paper presents a novel hybrid DDM for very short term (15 min-4 h) and short term (0-72 h) predictions of the wave energy power, based on the long-short term memory (LSTM) network and the results are compared with those obtained by the Artificial neural networks (ANN) and the support vector machine. The experimental results indicate that the proposed deep learning models enjoy a better performance with a high accuracy in the WEC power prediction than other related models. Furthermore, the proposed DDM methods are shown to be robust and timesaving in training and deployment, with advantages over the statistical methods in very short term and short term WEC power predictions.