Multiperiod‐Ahead Wind Speed Forecasting Using Deep Neural Architecture and Ensemble Learning

Chen, Lei; Li, Zhijun; Zhang, Yi

doi:10.1155/2019/9240317

Cited by 16 publications

(14 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results for this hybrid model are then compared with ARIMA, Back propagation neural network (BPNN) and Generalized regression neural network (GRNN). Chen et al explored deep learning and ensemble methods for multi‐step wind speed forecasting for a wind farm site in China 33 Wind speed time‐series with time intervals of 15‐minutes, 1 hour, 4 hours, 8 hours, and 24 hours for a duration of six months is taken. Stacked denoising autoencoder (SDAE) based feature extraction technique is employed in tandem with deep learning model like LSTM to predict wind speed.…”

Section: Introductionmentioning

confidence: 99%

Machine intelligent and deep learning techniques for large training data in short‐term wind speed and ramp event forecasting

Dhiman

Deb

2021

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Fluctuation in wind speed over the period of time can impact operation of a utility grid. Sudden large scale variation in turbine power, often termed as ramp event, can threaten the security of power system. In this work, we predict ramp events through a hybrid method based on discrete wavelet transform (DWT) and learning algorithms such as Twin Support vector regression (TSVR), random forest regression (RFR), and Convolutional neural networks (CNN) for onshore, offshore and hilly sites. Wavelet transform-based signal processing helps extract features from wind speed. Results suggest that SVR based prediction models are the best in forecasting among the available models. Besides, CNN predicts ramp events closer to the TSVR model and gives a better prediction performance for larger training datasets. The proposed hybrid version of TSVR, RFR, and CNN models are compared with existing SVM, ANN, and ELM models and indicate significant improvement in predicting ramp events. Compared to SVM, TSVR and RFR are 17.88% and 4.87% efficient in terms of RMSE, respectively. Further, randomness in ramp event signal for all the wind farm sites considered is evaluated using log-energy entropy. Results reveal that compared to wavelet transform, empirical mode decomposition yields lower randomness in predicted ramp event signals. K E Y W O R D Sempirical mode decomposition (EMD), ramp events, support vector regression (SVR), twin support vector regression (TSVR), wavelet transform (WT), wind forecasting | INTRODUCTIONEnvironmental deterioration and greenhouse gas emission is a cause of concern for many developing and developed countries. [1][2][3][4] Among renewable energy sources, by 2020, wind power is likely to cater 12% of global electricity

show abstract

Section: Introductionmentioning

confidence: 99%

Machine intelligent and deep learning techniques for large training data in short‐term wind speed and ramp event forecasting

Dhiman

Deb

2021

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…Since wind speed largely determines the amount of electricity generated by a turbine, accurate prediction can provide a reliable and secure source for the production of wind energy and also decrease the operating cost of the power system [3,5]. This also helps the power system to adapt the dispatch schedule in time according to changes in the input of wind power, ensures the quality of power, and reduces the cost of maintaining the service [6].…”

Section: Introductionmentioning

confidence: 99%

Application of Rough and Fuzzy Set Theory for Prediction of Stochastic Wind Speed Data Using Long Short-Term Memory

et al. 2021

View full text Add to dashboard Cite

Despite the great significance of precisely forecasting the wind speed for development of the new and clean energy technology and stable grid operators, the stochasticity of wind speed makes the prediction a complex and challenging task. For improving the security and economic performance of power grids, accurate short-term wind power forecasting is crucial. In this paper, a deep learning model (Long Short-term Memory (LSTM)) has been proposed for wind speed prediction. Knowing that wind speed time series is nonlinear stochastic, the mutual information (MI) approach was used to find the best subset from the data by maximizing the joint MI between subset and target output. To enhance the accuracy and reduce input characteristics and data uncertainties, rough set and interval type-2 fuzzy set theory are combined in the proposed deep learning model. Wind speed data from an international airport station in the southern coast of Iran Bandar-Abbas City was used as the original input dataset for the optimized deep learning model. Based on the statistical results, the rough set LSTM (RST-LSTM) model showed better prediction accuracy than fuzzy and original LSTM, as well as traditional neural networks, with the lowest error for training and testing datasets in different time horizons. The suggested model can support the optimization of the control approach and the smooth procedure of power system. The results confirm the superior capabilities of deep learning techniques for wind speed forecasting, which could also inspire new applications in meteorology assessment.

show abstract

“…Back propagation and support vector machine methods and their hybrids with empirical mode decomposition and wavelet transform, and an ensemble of the methods were used to predict wind speed in [30] and found that ensemble approaches predict better than the individual methods. A method which used analysis of variance to classify wind data into different categories, used stacked de-noising auto-encoder for training the classi ed data and nally used extreme learning machine to ne-tune and forecast from the trained model was proposed in [31]. Their method predicted better than the adaptive neuron-fuzzy inference system.…”

Section: Introductionmentioning

confidence: 99%

A Novel Integration of Hodrick-Prescott Filter and Harmonic Analysis with Machine Learning Methods to Enhance Time Series Prediction Accuracy of Daily and Monthly Wind Speeds

Ozoegwu

2021

Preprint

View full text Add to dashboard Cite

In this work, a new hybrid algorithm for modelling time series of daily and monthly wind speed is proposed. The method utilizes Hodrick-Prescott Filter (HPF) to decompose raw wind speed data into trend and cyclic components, and harmonic analysis (HA) is thereafter used to decompose the cyclic component into the periodic and stochastic sub-components. Machine learning (ML) methods are then used to model the time series of both the trend and stochastic components. The predicted wind speeds are finally summed from the individual predictions of the ML methods and harmonic analyses. To highlight the considerably higher predictive accuracy that results from the introduced data pre-treatments with HPF and HA, the proposed hybrid algorithm is compared against the traditional ML methods that are not subjected to the pre-treatments. The proposed hybrid algorithms are highly accurate relative to the traditional ML methods reflecting much higher coefficients of determination and correlation coefficients, and much lower error indices. Artificial neural networks (ANNs), linear regression with interactions (LRI), support vector machine (SVM), rational quadratic Gaussian process regression (RQGPR), fine regression trees (FRTs) and boosted ensembles of trees (BETs) are used as the illustrative machine learning methods. To guarantee both versatility and robustness, the methods are tested on example data drawn from both temperate and tropical conditions.

show abstract

Multiperiod‐Ahead Wind Speed Forecasting Using Deep Neural Architecture and Ensemble Learning

Cited by 16 publications

References 25 publications

Machine intelligent and deep learning techniques for large training data in short‐term wind speed and ramp event forecasting

Machine intelligent and deep learning techniques for large training data in short‐term wind speed and ramp event forecasting

Application of Rough and Fuzzy Set Theory for Prediction of Stochastic Wind Speed Data Using Long Short-Term Memory

A Novel Integration of Hodrick-Prescott Filter and Harmonic Analysis with Machine Learning Methods to Enhance Time Series Prediction Accuracy of Daily and Monthly Wind Speeds

Contact Info

Product

Resources

About