A Short-Term Wind Speed Forecasting Model Based on a Multi-Variable Long Short-Term Memory Network

Xie, Anqi; Yang, Hao; Chen, Jing; Li, Sheng; Zhang, Qian

doi:10.3390/atmos12050651

Cited by 45 publications

(19 citation statements)

References 36 publications

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“…To forecast short-term wind speed, three different models were proposed (ARIMA, LSTM, and multi-variable long short-term memory (MV-LSTM)). The results demonstrate that the prediction performance of the MV-LSTM model is superior to that of the traditional ARIMA method and the single-variable LSTM network [28]. The LSTM model was compared with another model with a recurrent neural network by training them using the same data.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comparative Analysis of the ARIMA and LSTM Predictive Models and Their Effectiveness for Predicting Wind Speed

Elsaraiti

Merabet

2021

Energies

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Forecasting wind speed has become one of the most attractive topics to researchers in the field of renewable energy due to its use in generating clean energy, and the capacity for integrating it into the electric grid. There are several methods and models for time series forecasting at the present time. Advancements in deep learning methods characterize the possibility of establishing a more developed multistep prediction model than shallow neural networks (SNNs). However, the accuracy and adequacy of long-term wind speed prediction is not yet well resolved. This study aims to find the most effective predictive model for time series, with less errors and higher accuracy in the predictions, using artificial neural networks (ANNs), recurrent neural networks (RNNs), and long short-term memory (LSTM), which is a special type of RNN model, compared to the common autoregressive integrated moving average (ARIMA). The results are measured by the root mean square error (RMSE) method. The comparison result shows that the LSTM method is more accurate than ARIMA.

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Section: Literature Reviewmentioning

confidence: 99%

A Comparative Analysis of the ARIMA and LSTM Predictive Models and Their Effectiveness for Predicting Wind Speed

Elsaraiti

Merabet

2021

Energies

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“…The Pearson correlation coefficient method is a measure of correlation between characteristics [13]. It is calculated as shown in Equation ( 16).…”

Section: Peterson Correlation Coefficient Methodsmentioning

confidence: 99%

Application of Bayesian Optimization and Stacking Integration in Personal Credit Delinquency Prediction

Yang¹,

Yin²

2022

Computer Science &Amp; Technology Trends

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The national concept of consumption has changed to excessive consumption, and overdue debts have also increased. The surge of non-performing loans will not only lead to the liquidity difficulties of banks, but also lead to financial risks. Accurate prediction of personal credit overdue is one of the key issues to control financial risks. Traditional machine learning methods build classification models according to the characteristics of credit users, while ensemble learning can ensure high accuracy and prevent model overfitting, which is the mainstream of current application research. The Stacking method can fully combine the advantages of the base model and improve the model performance. The base model and hyperparameter selection have great influence on the prediction accuracy. Therefore, parameter selection according to the studied problem is the core of application. In this paper, the Stacking method is used to integrate multiple single models for credit user overdue prediction, and the parameters of the base model are optimized. The improved Bayesian optimization method is used to select appropriate parameter combinations to improve the model performance.

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“…The LSTM network is a temporal convolutional neural network derived from RNN and was first proposed by Hochreiter and Schmidhuber [23]. LSTM has been broadly used in the field [24] of time series forecasting due to its outstanding advantages in dealing with longdistance dependency problems and reducing the learning difficulty of RNN.…”

Section: B Long Short-term Memorymentioning

confidence: 99%

Urban Water Supply Forecasting Based on CNN-LSTM-AM Spatiotemporal Deep Learning Model

Zhao,

Xu,

et al. 2023

IEEE Access

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Accurate and efficient forecasting of urban water supply is of great significance for urban water supply management. In this paper, a spatiotemporal deep learning model that integrates convolutional neural network (CNN), long short-term memory (LSTM), and attention mechanism (AM) is proposed for predicting the urban daily water supply. First, a one-dimensional CNN is used to identify the potential pattern structure in the water supply system and automatically extract the spatial features of the water supply data. Second, the feature vector output from the CNN is constructed into time series form and used as input to the LSTM network, and the parameters of the LSTM network are searched and optimized using the Bayesian algorithm. Then, the AM is introduced into the LSTM network, and the weighted sum is obtained by assigning the weights to the hidden layers of the LSTM network. Finally, the constructed CNN-LSTM-AM model captures the spatiotemporal information of the water supply data and makes an accurate prediction. Results show that the proposed CNN-LSTM-AM model reduces the mean absolute error, mean square error, and root mean square error values for two different sets of water supply data compared with the traditional LSTM, CNN-LSTM, and LSTM-AM models. The model has high forecasting accuracy and robustness, which are attributed to the excellent spatiotemporal feature extraction.

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A Short-Term Wind Speed Forecasting Model Based on a Multi-Variable Long Short-Term Memory Network

Cited by 45 publications

References 36 publications

A Comparative Analysis of the ARIMA and LSTM Predictive Models and Their Effectiveness for Predicting Wind Speed

A Comparative Analysis of the ARIMA and LSTM Predictive Models and Their Effectiveness for Predicting Wind Speed

Application of Bayesian Optimization and Stacking Integration in Personal Credit Delinquency Prediction

Urban Water Supply Forecasting Based on CNN-LSTM-AM Spatiotemporal Deep Learning Model

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