Fossil fuels cause environmental and ecosystem problems. Hence, fossil fuels are replaced by nonpolluting, renewable, and clean energy sources such as wind energy. The stochastic and intermittent nature of wind speed makes it challenging to obtain accurate predictions. Long short term memory (LSTM) networks are proved to be reliable models for time series forecasting. Hence, an improved deep learning-based hybrid framework to forecast wind speed is proposed in this paper. The new framework employs a stacked autoencoder (SAE) and a stacked LSTM network. The stacked autoencoder extracts more profound and abstract features from the original wind speed dataset. Empirical tests are conducted to identify an optimal stacked LSTM network. The extracted features from the SAE are then transferred to the optimal stacked LSTM network for predicting wind speed. The efficiency of the proposed hybrid model is compared with machine learning models such as support vector regression, artificial neural networks, and deep learning based models such as recurrent neural networks and long short term memory networks. Statistical error indicators, namely, mean absolute error, root mean squared error, and R2, are adopted to assess the performance of the models. The simulation results demonstrate that the suggested hybrid model produces more accurate forecasts.

中文翻译：

---

使用堆叠自编码器和 LSTM 的混合风速预测模型

化石燃料会导致环境和生态系统问题。因此，化石燃料被无污染、可再生和清洁的能源（如风能）所取代。风速的随机性和间歇性使得获得准确预测具有挑战性。长短期记忆 (LSTM) 网络已被证明是时间序列预测的可靠模型。因此，本文提出了一种改进的基于深度学习的混合框架来预测风速。新框架采用堆叠式自动编码器 (SAE) 和堆叠式 LSTM 网络。堆叠自编码器从原始风速数据集中提取更深刻和抽象的特征。进行实证测试以确定最佳堆叠 LSTM 网络。然后将从 SAE 中提取的特征转移到最优堆叠 LSTM 网络以预测风速。将所提出的混合模型的效率与机器学习模型（例如支持向量回归、人工神经网络）和基于深度学习的模型（例如循环神经网络和长短期记忆网络）进行比较。采用统计误差指标，即平均绝对误差、均方根误差和 R2 来评估模型的性能。模拟结果表明，建议的混合模型产生更准确的预测。用于评估模型的性能。模拟结果表明，建议的混合模型产生更准确的预测。用于评估模型的性能。模拟结果表明，建议的混合模型产生更准确的预测。