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A Multi-Scale Model based on the Long Short-Term Memory for day ahead hourly wind speed forecasting

机译：基于长短短期记忆的多尺度模型，日期前一小时风速预测

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摘要

Crucial to wind energy penetration in electrical systems is the precise forecasting of wind speed, which turns into accurate future wind power estimates. Current trends in wind speed forecasting involve using Recurrent Neural Networks to model complex temporal dynamics in the time-series. These networks, however, have problems learning long temporal dependencies in the data. To address this issue, we devise a Multi-scale Model Based on the Long Short-Term Memory for the day-ahead hourly wind speed forecasting task. Our model uses dense layers to build sub-sequences of different timescales which are used as input for multiple Long Short-Term Memory Networks (LSTM), which model each temporal scale and integrate their information accordingly. An experiment with altered wind speed data shows that our proposal is better able to learn long term dependencies than the stacked LSTM. Furthermore, results on four wind speed datasets of varying length from northern Chile reveal that our approach outperforms several models in terms of MAE and RMSE. Training times also exhibit that adding depth to the model does not increase computational times substantially, making it a more efficient approach than the stacked LSTM.
机译：在电气系统中风能渗透至关重要，是风速的精确预测，这变成了准确的未来风电估算。风速预测的当前趋势涉及使用经常性神经网络在时间序列中模拟复杂的时间动态。然而，这些网络在数据中学习了长时间依赖性的问题。为了解决这个问题，我们根据日期时间风速预测任务的长短短期记忆设计了一个多尺度模型。我们的模型使用密集层来构建不同时间尺度的子序列，该次序列用作多个长短期存储器网络（LSTM）的输入，该输入为每个时间尺度和相应地集成其信息。具有改变的风速数据的实验表明，我们的建议更能学习比堆叠的LSTM长期依赖性。此外，结果来自智利北部的四个风速数据集，揭示了我们的方法在MAE和RMSE方面优于多种型号。培训时间还表现出模型的增加深度不会显着增加计算时间，使其比堆叠的LSTM更有效的方法。

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