ABSTRACT

Very short-term offshore wind speed forecasting by application of Stacked long short-term memory (LSTM) and Bidirectional LSTM deep learning models is done in this work. Wind speed data of two different offshore sites located in two different continents are used for testing the models. Performance is measured on the basis of accuracy of forecasting and computational time. The effectiveness of Stacked LSTM and Bidirectional LSTM models is also validated by comparing their performance with convolutional neural network, convolutional neural network-long short-term memory network, multi-layer perceptron, and rolling forecasting auto regressive integrated moving average models. Results of forecasting error confirm that Stacked LSTM model is better than other compared models in forecasting very short-term offshore wind speed. Mean absolute percentage error (MAPE) of wind speed forecasting by Stacked LSTM model is 4.59% at Anholt (Denmark) and 3.62% at Dhanushkodi (India) sites. From comparison of MAPE of Stacked LSTM model with that of eight other latest existing models in literature, it can be concluded that Stacked LSTM model is superior to many other existing models.

摘要

在这项工作中完成了通过应用堆叠长短期记忆 (LSTM) 和双向 LSTM 深度学习模型进行的极短期海上风速预测。位于两个不同大陆的两个不同海上站点的风速数据用于测试模型。性能是根据预测的准确性和计算时间来衡量的。Stacked LSTM 和 Bidirectional LSTM 模型的有效性也通过将它们与卷积神经网络、卷积神经网络-长短期记忆网络、多层感知器和滚动预测自回归集成移动平均模型的性能进行比较来验证。预测误差结果证实，Stacked LSTM 模型在预测极短期海上风速方面优于其他比较模型。Stacked LSTM 模型风速预测的平均绝对百分比误差 (MAPE) 在 Anholt（丹麦）为 4.59%，在 Dhanushkodi（印度）站点为 3.62%。从 Stacked LSTM 模型的 MAPE 与文献中其他八个最新现有模型的 MAPE 的比较可以得出结论，Stacked LSTM 模型优于许多其他现有模型。