Multistep Wind Speed and Wind Power Prediction Based on a Predictive Deep Belief Network and an Optimized Random Forest

Sun, Zexian; Sun, Hexu; Zhang, Jingxuan

doi:10.1155/2018/6231745

Cited by 28 publications

(15 citation statements)

References 31 publications

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“…The result is recorded to have a 41% decrease in an error rate that needs human intervention as compared to no bootstrapping implementation. Sun et al [20] predict wind speed and wind power using deep belief network and optimized random forest. The experiment has an inconsistent amount of data because some data are simply unavailable.…”

Section: A R T I C L E I N F Omentioning

confidence: 99%

Evolutionary deep belief networks with bootstrap sampling for imbalanced class datasets

Amri

Ismail

Mohammad

2019

Int. J. Adv. Intell. Informatics

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Imbalanced class data is a common issue faced in classification tasks. Deep Belief Networks (DBN) is a promising deep learning algorithm when learning from complex feature input. However, when handling imbalanced class data, DBN encounters low performance as other machine learning algorithms. In this paper, the genetic algorithm (GA) and bootstrap sampling are incorporated into DBN to lessen the drawbacks occurs when imbalanced class datasets are used. The performance of the proposed algorithm is compared with DBN and is evaluated using performance metrics. The results showed that there is an improvement in performance when Evolutionary DBN with bootstrap sampling is used to handle imbalanced class datasets.

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Section: A R T I C L E I N F Omentioning

confidence: 99%

Evolutionary deep belief networks with bootstrap sampling for imbalanced class datasets

Amri

Ismail

Mohammad

2019

Int. J. Adv. Intell. Informatics

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“…Each sublayer is predicted separately by the prediction model, and the results of all sublayers are then recombined to obtain the final predicted values [13,14]. e second construction usually uses several different prediction models to predict the wind power separately and then recombines the results to obtain the final predicted values [15,16]. Because hybrid methods have a better predictive performance, research with this approach has peaked in recent years.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Method for Ultrashort-Term Wind Power Prediction considering Meteorological Features and Seasonal Information

Zhou

Sun

et al. 2020

Mathematical Problems in Engineering

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High-precision wind power prediction is important for the planning, economics, and security maintenance of a power grid. Meteorological features and seasonal information are strongly related to wind power prediction. This paper proposes a hybrid method for ultrashort-term wind power prediction considering meteorological features (wind direction, wind speed, temperature, atmospheric pressure, and humidity) and seasonal information. The wind power data are decomposed into stationary subsequences using the ensemble empirical mode decomposition (EEMD). The principal component analysis (PCA) is used to reduce the redundant meteorological features and the algorithm complexity. With the stationary subsequences and extracted meteorological features data as inputs, the long short-term memory (LSTM) network is used to complete the wind power prediction. Finally, the seasonal autoregressive integrated moving average (SARIMA) is innovatively used to fit seasonal features (quarterly and monthly) of wind power and reconstruct the prediction results of LSTM. The proposed method is used to predict 15-minute wind power. In this study, three datasets were collected from a windfarm in Laizhou to validate the prediction performance of the proposed method. The experimental results showed that the prediction accuracy was significantly improved when meteorological features were considered and further improved with seasonal correction.

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“…In [10], fractional-ARIMA models forecast wind speeds on the day-ahead and two-day-ahead horizons in North Dakota. Generally, artificial intelligence methods include artificial neural networks (ANN) [13], support vector machines (SVM) [14], [15], and extreme learning machines (ELM) [16], [17], random forest [18]. In [14], Using V-SVM which parameters are optimized using the Cuckoo search algorithm predict wind speed and the Grey correlation analysis is used to determine the input set.…”

Section: Introductionmentioning

confidence: 99%

“…In [17], the stacked ELM is used to predict wind speed which is an advanced ELM algorithm under deep learning framework. In [18], the random forest is used as supervised forecasting model and a data driven dimension reduction procedure and a weighted voting method are utilized to optimize the random forest algorithm in the training process and the prediction process.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Wind Speed Forecast With Low Loss of Information Based on Feature Generation of OSVD

Huang

Cai

et al. 2019

IEEE Access

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Improving the accuracy of wind speed forecast can reduce the randomness and uncertainty of the wind power output and effectively improve a system's wind power accommodation. However, the highdimensional historical wind speed information should be taken into account in the wind speed forecast, which increases the complexity of the model and reduces the efficiency and accuracy of a forecast. Feature selection by the Filter method can effectively reduce the feature dimension, but losing all the information of low-importance features. Although the feature reduction can retain the partial information of all features, it causes the loss of the partial information of high-importance features. In order to reduce the information loss caused by traditional FS and FR, short-term wind speed forecast with low information loss based on OSVD feature generation is proposed. First, the original wind speed series is denoised by OVMD. Then, based on the 96-dimensional original wind speed feature set, the OSVD is used to generate features. Furthermore, the extended original feature set EFS is obtained by combining the initial feature set with the features generated by OSVD. Gini importance is used to measure the importance of all features in EFS, and the forward feature selection is combined with random forests to determine the optimal subset. Finally, the optimal model determined by the new method is compared with seven models to verify the advancement of the new method. The experiments show that it reduces the information loss. Thus, the model has a higher forecast accuracy than the traditional model. INDEX TERMS Short-term wind speed forecast, feature selection, feature reduction, low information loss, variational mode decomposition, singular value decomposition. The associate editor coordinating the review of this manuscript and approving it for publication was Shuaihu Li. which makes the safe and reliable operation of a power grid enormously challenging and restricts the development of wind power. Accurate and efficient wind speed forecast (WSF) can reduce the negative impact of wind power uncertainty [4], [5]. WSF methods can generally be divided into physical methods [6], [7], statistical methods [8]-[11], artificial intelligence (AI) methods [12]-[17], and others. The physical method is based on a numerical weather prediction (NWP) model, which uses a series of meteorological data (wind direction, temperature, and humidity) and terrain information to establish WSFM [6], [7]. Unlike physical methods, statistical methods only use historical

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Multistep Wind Speed and Wind Power Prediction Based on a Predictive Deep Belief Network and an Optimized Random Forest

Cited by 28 publications

References 31 publications

Evolutionary deep belief networks with bootstrap sampling for imbalanced class datasets

Evolutionary deep belief networks with bootstrap sampling for imbalanced class datasets

A Hybrid Method for Ultrashort-Term Wind Power Prediction considering Meteorological Features and Seasonal Information

Short-Term Wind Speed Forecast With Low Loss of Information Based on Feature Generation of OSVD

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