Predicting Inflow Rate of the Soyang River Dam Using Deep Learning Techniques

Lee, Sang‐Won; Kim, Jaekwang

doi:10.3390/w13172447

Cited by 21 publications

(9 citation statements)

References 31 publications

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“…In [25], a model combining ARIMA and dynamic evolving neural-fuzzy inference system (DENFIS) for wind speed forecasting has been proposed, reporting RMSE values of 0.07 and 0.13 for 2 and 6 hour ahead forecasting, respectively. As a reported drawback, DENFIS needs prior assumptions about data and requires domain knowledge to define the associated parameters [26]. Other ML-based models have been proposed to improve the forecasting accuracy.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Forecasting of Wind Energy: A Comparison of Deep Learning Frameworks

2021

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Wind energy has been recognized as the most promising and economical renewable energy source, attracting increasing attention in recent years. However, considering the variability and uncertainty of wind energy, accurate forecasting is crucial to propel high levels of wind energy penetration within electricity markets. In this paper, a comparative framework is proposed where a suite of long short-term memory (LSTM) recurrent neural networks (RNN) models, inclusive of standard, bidirectional, stacked, convolutional, and autoencoder architectures, are implemented to address the existing gaps and limitations of reported wind power forecasting methodologies. These integrated networks are implemented through an iterative process of varying hyperparameters to better assess their effect, and the overall performance of each architecture, when tackling one-hour to three-hours ahead wind power forecasting. The corresponding validation is carried out through hourly wind power data from the Spanish electricity market, collected between 2014 and 2020. The proposed comparative error analysis shows that, overall, the models tend to showcase low error variability and better performance when the networks are able to learn in weekly sequences. The model with the best performance in forecasting one-hour ahead wind power is the stacked LSTM, implemented with weekly learning input sequences, with an average MAPE improvement of roughly 6, 7, and 49%, when compared to standard, bidirectional, and convolutional LSTM models, respectively. In the case of two to three-hours ahead forecasting, the model with the best overall performance is the bidirectional LSTM implemented with weekly learning input sequences, showcasing an average improved MAPE performance from 2 to 23% when compared to the other LSTM architectures implemented.

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Section: Introductionmentioning

confidence: 99%

Short-Term Forecasting of Wind Energy: A Comparison of Deep Learning Frameworks

2021

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“…To solve this problem, machine learning methods have been applied to model them for efficient sustainability [ 5 ]. In an attempt to further increase these models' efficiency, deep learning neural network models have been applied to reservoir inflow forecasting with promising results [ [6] , [7] , [8] , [9] , [10] , [11] , [12] ] Recurrent Neural Networks (RNN), Gated Recurrent Unit (GRU), and Long-Short Tem Memory (LSTM) models have been used for reservoir inflow forecasting, with LSTM proving to be the most effective [ 13 ]. A hybrid framework using machine learning for reservoir inflow forecast has been proposed by Tian et al [ 14 ] with interesting results as an outcome as compared to classical methods.…”

Section: Introductionmentioning

confidence: 99%

STL-decomposition ensemble deep learning models for daily reservoir inflow forecast for hydroelectricity production

Tebong

Simo

Takougang

et al. 2023

Heliyon

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“…Le et al [29] evaluated the performance of several deep learning models for streamflow forecasting, and the study shows that the LSTM-based models have better performance and stability than the feedforward neural network (FFNN) and convolutional neural network (CNN) models. Lee and Kim [30] predicted the inflow rate with a sequence-to-sequence mechanism combined with a bidirectional LSTM. Li et al [31] used a deep-stacked bidirectional LSTM neural network with a self-attention mechanism to capture the temporal dependencies of the original sensor data, and the method can deal with various missing data scenarios in dam monitoring system.…”

Section: Introductionmentioning

confidence: 99%

LSTM-Based Deformation Prediction Model of the Embankment Dam of the Danjiangkou Hydropower Station

Wang¹,

Yang²,

Chen³

et al. 2022

Water

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The Danjiangkou hydropower station is a water source project for the middle line of the South-to-North Water Transfer Project in China. The dam is composed of riverbed concrete dam and earth rock dam on both banks, with a total length of 3442 m. Once the dam is wrecked, it will yield disastrous consequences. Therefore, it is very important to evaluate the dam safety behavior in time. Based on the long-term and short-term memory (LSTM) network, the deformation prediction models of the embankment dam of the Danjiangkou hydropower station are constructed. The models contain two LSTM layers, adopt the rectified linear unit function as the activation function and determine the super parameters of the models with Bayesian optimization algorithm. According to the settlement monitoring data of LD12ZT01 measuring point (dam crest 0 + 648) on the left bank of the embankment dam of the Danjiangkou hydropower station from July 2013 to March 2022, the LSTM and bidirectional LSTM models are constructed. In total, 80% of the monitoring data are taken as the training set data and 20% of the monitoring data are taken as the test set data. The mean absolute error, root mean square error and mean square error for the test set are 0.42978, 0.56456 and 0.31873 for partial least squares regression (PLSR), 0.35264, 0.47561 and 0.22621 for LSTM and 0.34418, 0.45400 and 0.20612 for bidirectional LSTM, respectively. The results show that the bidirectional LSTM model can obtain better deformation prediction value than the LSTM model and the PLSR. Then, the bidirectional LSTM model is used to predict the settlement value of LD16YT01 measuring point (dam crest 0 + 658) on the right bank, and the mean absolute error, root mean square error and mean square error for the test set are 0.5425, 0.66971 and 0.4520, respectively. This shows the bidirectional LSTM model can effectively predict the settlement value of the embankment dam of the Danjiangkou hydropower station.

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Predicting Inflow Rate of the Soyang River Dam Using Deep Learning Techniques

Cited by 21 publications

References 31 publications

Short-Term Forecasting of Wind Energy: A Comparison of Deep Learning Frameworks

Short-Term Forecasting of Wind Energy: A Comparison of Deep Learning Frameworks

STL-decomposition ensemble deep learning models for daily reservoir inflow forecast for hydroelectricity production

LSTM-Based Deformation Prediction Model of the Embankment Dam of the Danjiangkou Hydropower Station

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