Accurate prediction of wind power generation is complex due to stochastic component, but can play a significant role in minimizing operating costs, and improving reliability and security of a power system. This paper proposes a hybrid deep learning model to accurately forecast the very-short-term (5-min and 10-min) wind power generation of the Boco Rock Wind Farm in Australia. The model consists of a convolutional neural network, gated recurrent units (GRU) and a fully connected neural network. To improve performance, the hyper-parameters of the model are tuned using the Harris Hawks Optimization algorithm. The effectiveness of the proposed model is evaluated against other advanced models, including multilayer feedforward neural network (NN), recurrent neural network (RNN), long short-term memory (LSTM) and GRU. The forecasting model demonstrates around 38% and 24% higher accuracy as compared to the 5- and 10-min forecasting of the NN model, respectively.

中文翻译：

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使用带优化的混合深度学习预测风力发电

由于随机分量的存在，风力发电的准确预测是复杂的，但可以在最小化运行成本、提高电力系统的可靠性和安全性方面发挥重要作用。本文提出了一种混合深度学习模型来准确预测澳大利亚 Boco Rock 风电场的超短期（5 分钟和 10 分钟）风力发电量。该模型由卷积神经网络、门控循环单元（GRU）和全连接神经网络组成。为了提高性能，模型的超参数使用 Harris Hawks 优化算法进行了调整。所提出模型的有效性是针对其他高级模型进行评估的，包括多层前馈神经网络 (NN)、循环神经网络 (RNN)、长短期记忆 (LSTM) 和 GRU。