Short-Term Wind Speed Interval Prediction Based on Ensemble GRU Model

Li, Chaoshun; Tang, Geng; Xue, Xiaoming; Saeed, Adnan; Hu, Xin

doi:10.1109/tste.2019.2926147

Cited by 182 publications

(56 citation statements)

References 36 publications

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“…In addition, long short-term memory (LSTM) network is a variant of RNN, which overcomes the gradient disappearance and gradient explosion of RNN, therefore, the LSTM adopted by Liu et al performed more prominently on long sequences [18]. Other DNN baseline models were trained with abundant samples, such as gate recurrent unit (GRU) [19] and bidirectional recurrent neural network (Bi-RNN) [20], however their results implied that it is not a promising realisation due to the ease of overfitting. In the process of STLF development, single machine learning models had difficulty meeting load accuracy requirements and a few hybrid preprocessing methods were mixed into them.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting Based on PSO-KFCM Daily Load Curve Clustering and CNN-LSTM Model

et al. 2021

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Short-term load forecasting (STLF) with excellent precision and prominent efficiency plays a significant role in the stable operation of power grid and the improvement of economic benefits. In this paper, a novel model based on data mining and deep learning is proposed. Firstly, the preprocessing of data includes normalization of historical load, and fuzzification of influencing factors (meteorological factors, date types and economy) based on Pearson correlation coefficient (PCC). Secondly, kernel fuzzy c-means (KFCM) modified by particle swarm optimization (PSO-KFCM) algorithm clusters the daily load curve. In the clustering experiments, the within-cluster sum of squared error (SSE) index is presented to determine the number of clusters and the clustering validity has a 31.9% enhancement compared with the traditional FCM algorithm. Thirdly, the cosine similarity establishes the resemblance between the prediction date and each cluster, and the similar cluster is determined according to the principle of maximum similarity. Finally, a multivariate and multi-step hybrid model MMCNN-LSTM based on convolution neural network (CNN) and long short-term memory (LSTM) neural network is proposed to forecast the load in following 24 hours, in which similar cluster data is applied to training set. To demonstrate the effectiveness of proposed integrated technique, the accuracy has been verified in three predictive experiments. The fruitful results indicated that the average mean absolute percent error (MAPE) in the entire test set was only 1.34%, a 3.02% reduction compared to a single LSTM.

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Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting Based on PSO-KFCM Daily Load Curve Clustering and CNN-LSTM Model

et al. 2021

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“…Comparatively, the GD II model is 32.78% higher than GD I model, and it can be adjusted by the error correction mechanism 5 . Also, the GD III model is 5.34% lower than the GD II model, indicating that Adagrad has a certain negative effect on the deviation compared with the RMSprop training algorithm.…”

Section: Methodsmentioning

confidence: 94%

“…The prediction results can be in the form of a point, interval, 5 or probability interval 6 . It is currently believed that the outcomes of a time‐series prediction are better in sets of data rather than a single point value.…”

Section: Introductionmentioning

confidence: 99%

A new lower and upper bound estimation model using gradient descend training method for wind speed interval prediction

Liu

et al. 2020

Wind Energy

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As a clean and renewable energy source, wind energy has achieved remarkable growth around the world. Wind power/speed interval prediction has become an indispensable area of focus regarding the efficient dispatch of wind energy. As an important interval prediction method, the traditional lower and upper bound estimation (LUBE) has been a prevalent approach and a fundamental branch of energy prediction. However, the traditional LUBE model suffers from a low training efficiency owing to a lack of the gradient descent (GD) training mechanism. In this study, an improved LUBE model was designed using a novel training scheme based on the GD method for better efficiency and greater prediction performance. Initially, the new objective functions, which are continuous and differential, meeting the requirements of the GD method, were designed to obtain the best prediction interval (PI) quality with a narrower PI width and greater coverage probability. Then, different loss function forms have been proposed and compared, with the new Huber loss function having been confirmed to be more effective than other traditional loss functions. Finally, the new LUBE model with an objective part and adapting to the GD training method was constructed. Both traditional and improved LUBE models with different loss functions were compared experimentally, and the results indicate that the improved LUBE model with a Huber loss function significantly reduces the training time and improves the quality of the PI.

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“…For experiments we divide the wind series data into four subseries and evaluate the model performance in each of them. This is similar to performing a cross-validation to assure better generalization capabilities of the model [32]. Each subseries is extracted using a sliding window of 15 months, of which the first twelve months' data is considered for training the model while the remaining three months' data is kept for testing purpose, e.g., first subseries consists of data from January 2011 to December 2011 as train data and from January 2012 to March 2012 as test data, second subseries from April 2011 to March 2012 as train data and from April 2012 to June 2012 as test data and so on.…”

Section: Experiments On Real Datasets a Dataset And Methodologiesmentioning

confidence: 99%

Hybrid Bidirectional LSTM Model for Short-Term Wind Speed Interval Prediction

Saeed

Danish

et al. 2020

IEEE Access

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Wind speed interval prediction is gaining importance in optimal planning and operation of power systems. However, the unpredictable characteristics of wind energy makes quality forecasting an arduous task. In this paper, we propose a novel hybrid model for wind speed interval prediction using an autoencoder and a bidirectional long short term memory neural network. The autoencoder initially extracts important unseen features from the wind speed data. The artificially generated features are utilized as input to the bidirectional long short term memory neural network to generate the prediction intervals. We also demonstrate that for time series prediction tasks, feature extraction through autoencoder is more effective than making deep residual networks. In our experiments which involve eight cases distributed among two wind fields, the proposed method is able to generate narrow prediction intervals with high prediction interval coverage and achieve an improvement of 39% in coverage width criterion over the traditional models.

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Short-Term Wind Speed Interval Prediction Based on Ensemble GRU Model

Cited by 182 publications

References 36 publications

Short-Term Load Forecasting Based on PSO-KFCM Daily Load Curve Clustering and CNN-LSTM Model

Short-Term Load Forecasting Based on PSO-KFCM Daily Load Curve Clustering and CNN-LSTM Model

A new lower and upper bound estimation model using gradient descend training method for wind speed interval prediction

Hybrid Bidirectional LSTM Model for Short-Term Wind Speed Interval Prediction

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