Wind Power Forecasting Using Attention-Based Recurrent Neural Networks: A Comparative Study

Huang, Bin; Liang, Yuying; Qiu, Xiaolin

doi:10.1109/access.2021.3065502

Cited by 45 publications

(9 citation statements)

References 42 publications

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“…The RNN is the most widely used deep learning model for prediction, and it has gained a lot of traction. Recent years have seen a surge in approaches that use neural network structures to make the prediction results more accurate [17,21,38].…”

Section: Related Workmentioning

confidence: 99%

Civil airline fare prediction with a multi-attribute dual-stage attention mechanism

et al. 2021

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Airfare price prediction is one of the core facilities of the decision support system in civil aviation, which includes departure time, days of purchase in advance and flight airline. The traditional airfare price prediction system is limited by the nonlinear interrelationship of multiple factors and fails to deal with the impact of different time steps, resulting in low prediction accuracy. To address these challenges, this paper proposes a novel civil airline fare prediction system with a Multi-Attribute Dual-stage Attention (MADA) mechanism integrating different types of data extracted from the same dimension. In this method, the Seq2Seq model is used to add attention mechanisms to both the encoder and the decoder. The encoder attention mechanism extracts multi-attribute data from time series, which are optimized and filtered by the temporal attention mechanism in the decoder to capture the complex time dependence of the ticket price sequence. Extensive experiments with actual civil aviation data sets were performed, and the results suggested that MADA outperforms airfare prediction models based on the Auto-Regressive Integrated Moving Average (ARIMA), random forest, or deep learning models in MSE, RMSE, and MAE indicators. And from the results of a large amount of experimental data, it is proven that the prediction results of the MADA model proposed in this paper on different routes are at least 2.3% better than the other compared models.

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Section: Related Workmentioning

confidence: 99%

Civil airline fare prediction with a multi-attribute dual-stage attention mechanism

et al. 2021

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“…Furthermore, LSTNet adopts the traditional autoregressive (AR) model to tackle the scale-insensitive problem of the neural network model. LSTNet has been applied to wind power prediction [10], household load prediction [11], and temperature prediction [12]. In [10], the performance of models such as LSTNet, temporal pattern attention-based long short-term memory (TPA-LSTM), and dual-stage attention-based recurrent neural network (DA-RNN) was compared and analyzed for the wind power prediction problem.…”

Section: Introductionmentioning

confidence: 99%

“…LSTNet has been applied to wind power prediction [10], household load prediction [11], and temperature prediction [12]. In [10], the performance of models such as LSTNet, temporal pattern attention-based long short-term memory (TPA-LSTM), and dual-stage attention-based recurrent neural network (DA-RNN) was compared and analyzed for the wind power prediction problem. However, the problem of wind and solar power forecasting based on LSTNet has not been sufciently studied.…”

Section: Introductionmentioning

confidence: 99%

Short‐Term Wind and Solar Power Prediction Based on Feature Selection and Improved Long‐ and Short‐Term Time‐Series Networks

Wang

et al. 2023

Mathematical Problems in Engineering

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In terms of the problems of high feature dimension and large data redundancy in the wind and solar power prediction method, an improved prediction model is proposed by combining feature selection methods with the long- and short-term time-series network (LSTNet). The long short-term memory (LSTM) unit in the LSTNet model is replaced with the bidirectional long short-term memory (BiLSTM), which enables recursive response training for the states of hidden layers at the start and end of the sequence. For feature selection, both feature screening and dimension reduction methods are considered, including random forest (RF), grey relational analysis (GRA), and principal component analysis (PCA). Finally, based on wind and solar power data, the effectiveness of the proposed methods is verified, where the RF-LSTNet performs the best. For wind power prediction, the mean absolute percentage error is reduced by 29.7% and root mean square error is reduced by 24.1% compared with the traditional LSTNet model, and for solar power prediction, the MAPE is reduced by 12.9% and RMSE is reduced by 3.8%.

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“…Qin et al [31] developed a hybrid optimization technique which combined a firefly algorithm, long short-term memory (LSTM) neural network, minimum redundancy algorithm (MRA), and variational mode decomposition (VMD) to improve wind power forecasting accuracy. Huang et al [32] used an artificial recurrent neural network for forecasting. Recently, some researchers have developed their own optimization approaches, such as in [33,34], where the authors developed sequence transfer correction and rolling long short-term memory (R-LSTM) algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments

et al. 2021

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In the last few years, several countries have accomplished their determined renewable energy targets to achieve their future energy requirements with the foremost aim to encourage sustainable growth with reduced emissions, mainly through the implementation of wind and solar energy. In the present study, we propose and compare five optimized robust regression machine learning methods, namely, random forest, gradient boosting machine (GBM), k-nearest neighbor (kNN), decision-tree, and extra tree regression, which are applied to improve the forecasting accuracy of short-term wind energy generation in the Turkish wind farms, situated in the west of Turkey, on the basis of a historic data of the wind speed and direction. Polar diagrams are plotted and the impacts of input variables such as the wind speed and direction on the wind energy generation are examined. Scatter curves depicting relationships between the wind speed and the produced turbine power are plotted for all of the methods and the predicted average wind power is compared with the real average power from the turbine with the help of the plotted error curves. The results demonstrate the superior forecasting performance of the algorithm incorporating gradient boosting machine regression.

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Wind Power Forecasting Using Attention-Based Recurrent Neural Networks: A Comparative Study

Cited by 45 publications

References 42 publications

Civil airline fare prediction with a multi-attribute dual-stage attention mechanism

Civil airline fare prediction with a multi-attribute dual-stage attention mechanism

Short‐Term Wind and Solar Power Prediction Based on Feature Selection and Improved Long‐ and Short‐Term Time‐Series Networks

A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments

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