EALSTM-QR: Interval wind-power prediction model based on numerical weather prediction and deep learning

Peng, Xiaosheng; Wang, Hongyu; Lang, Jianxun; Li, Wenze; Xu, Q.; Zhang, Zuowei; Cai, Tao; Duan, Shanxu; Liu, Fangjie; Li, Chaoshun

doi:10.1016/j.energy.2020.119692

Cited by 89 publications

(35 citation statements)

References 26 publications

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“…To verify the effectiveness of the proposed MOGPR, different prediction models are compared, including two multiobjective optimization machine learning models named MOQRLSTM and MOQRNN, two machine learning models without parameter optimization named QRLSTM and QRNN [39], three nonparametric statistical regression models named locally weighted linear regression (LWLR) [40] and kernel regression (KR) [41]. The loss functions of QRLSTM and QRNN are defined as quantile regression functions [42], which makes them predict conditional quantiles.…”

Section: Comparison Of Different Prediction Methodsmentioning

confidence: 99%

An Ensemble Prognostic Method of Francis Turbine Units Using Low-Quality Data under Variable Operating Conditions

Duan

Liu

Zhou

et al. 2022

Sensors

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The prognostic is the key to the state-based maintenance of Francis turbine units (FTUs), which consists of performance state evaluation and degradation trend prediction. In practical engineering environments, there are three significant difficulties: low data quality, complex variable operation conditions, and prediction model parameter optimization. In order to effectively solve the above three problems, an ensemble prognostic method of FTUs using low-quality data under variable operation conditions is proposed in this study. Firstly, to consider the operation condition parameters, the running data set of the FTU is constructed by the water head, active power, and vibration amplitude of the top cover. Then, to improve the robustness of the proposed model against anomaly data, the density-based spatial clustering of applications with noise (DBSCAN) is introduced to clean outliers and singularities in the raw running data set. Next, considering the randomness of the monitoring data, the healthy state model based on the Gaussian mixture model is constructed, and the negative log-likelihood probability is calculated as the performance degradation indicator (PDI). Furthermore, to predict the trend of PDIs with confidence interval and automatically optimize the prediction model on both accuracy and certainty, the multiobjective prediction model is proposed based on the non-dominated sorting genetic algorithm and Gaussian process regression. Finally, monitoring data from an actual large FTU was used for effectiveness verification. The stability and smoothness of the PDI curve are improved by 3.2 times and 1.9 times, respectively, by DBSCAN compared with 3-sigma. The root-mean-squared error, the prediction interval normalized average, the prediction interval coverage probability, the mean absolute percentage error, and the R2 score of the proposed method achieved 0.223, 0.289, 1.000, 0.641%, and 0.974, respectively. The comparison experiments demonstrate that the proposed method is more robust to low-quality data and has better accuracy, certainty, and reliability for the prognostic of the FTU under complex operating conditions.

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Section: Comparison Of Different Prediction Methodsmentioning

confidence: 99%

An Ensemble Prognostic Method of Francis Turbine Units Using Low-Quality Data under Variable Operating Conditions

Duan

Liu

Zhou

et al. 2022

Sensors

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“…Ensemble spread is mainly built by randomizing the CNN training procedure for creating a set of 32 DLWP methods. In Peng et al [18], a novel NN predictive model named EALSTM-QR has been proposed to predict the wind power considering the input of NWP and the DL methods. In this method, there are four major steps namely Attention, Encoder, bi-LSTM and QR.…”

Section: Prior Weather Forecasting Approachesmentioning

confidence: 99%

Hyperparameter Tuned Bidirectional Gated Recurrent Neural Network for Weather Forecasting

Manikandan¹,

Nagaraj²

2022

Intelligent Automation &Amp; Soft Computing

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Weather forecasting is primarily related to the prediction of weather conditions that becomes highly important in diverse applications like drought discovery, severe weather forecast, climate monitoring, agriculture, aviation, telecommunication, etc. Data-driven computer modelling with Artificial Neural Networks (ANN) can be used to solve non-linear problems. Presently, Deep Learning (DL) based weather forecasting models can be designed to accomplish reasonable predictive performance. In this aspect, this study presents a Hyper Parameter Tuned Bidirectional Gated Recurrent Neural Network (HPT-BiGRNN) technique for weather forecasting. The HPT-BiGRNN technique aims to utilize the past weather data for training the BiGRNN model and achieve the effective forecasts with minimum time duration. The BiGRNN is an enhanced version of Gated Recurrent Unit (GRU) that follows the process of passing input via forward and backward neural network and the outputs are linked to the identical output layer. The BiGRNN technique includes several hyper-parameters and hence, the hyperparameter optimization process takes place using Bird Mating Optimizer (BMO). The design of BMO algorithm for hyperparameter optimization of the BiGRNN, particularly for weather forecast shows the novelty of the work. The BMO algorithm is used to set hyperparameters such as momentum, learning rate, batch size and weight decay. The experimental result the HPT-BiGRNN approach has resulted in a lower RMSE of 0.173 whereas the Fuzzy-GP, Fuzzy-SC, MLP-ANN and RBF-ANN methods have gained an increased RMSE of 0.218, 0.216, 0.202 and 0.245 respectively.

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“…Zhou (Zhou et al, 2019) proposed a K-means-long short-term memory (K-means-LSTM) neural network to classify wind power factors and establish a sub-prediction model. Peng (Peng et al, 2021) proposed a new neural-network prediction model named encoder attention BiLSTM-quantile regression (EALSTM-QR), which was developed for wind-power prediction considering the input of NWP and the deep-learning method. The combination inputs contain historical wind-power data and features extracted and obtained from the NWP through the encoder and attention levels.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Forecasting Model Based on CNN and Informer for Short-Term Wind Power

2022

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Wind power prediction reduces the uncertainty of an entire energy system, which is very important for balancing energy supply and demand. To improve the prediction accuracy, an average wind power prediction method based on a convolutional neural network and a model named Informer is proposed. The original data features comprise only one time scale, which has a minimal amount of time information and trends. A 2-D convolutional neural network was employed to extract additional time features and trend information. To improve the accuracy of long sequence input prediction, Informer is applied to predict the average wind power. The proposed model was trained and tested based on a dataset of a real wind farm in a region of China. The evaluation metrics included MAE, MSE, RMSE, and MAPE. Many experimental results show that the proposed methods achieve good performance and effectively improve the average wind power prediction accuracy.

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EALSTM-QR: Interval wind-power prediction model based on numerical weather prediction and deep learning

Cited by 89 publications

References 26 publications

An Ensemble Prognostic Method of Francis Turbine Units Using Low-Quality Data under Variable Operating Conditions

An Ensemble Prognostic Method of Francis Turbine Units Using Low-Quality Data under Variable Operating Conditions

Hyperparameter Tuned Bidirectional Gated Recurrent Neural Network for Weather Forecasting

A Hybrid Forecasting Model Based on CNN and Informer for Short-Term Wind Power

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