An improved random forest model of short‐term wind‐power forecasting to enhance accuracy, efficiency, and robustness

Shi, Kunpeng; Qiao, Ying; Zhao, Wei; Wang, Qing; Liu, Menghua; Lu, Zongxiang

doi:10.1002/we.2261

Cited by 52 publications

(31 citation statements)

References 30 publications

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“…In the Bagging method of Random Forest, about 1/3 of the samples will not in the sample set collected by Bootstrap each time, which is called Out-Of-Bag (OOB), which can be used as generalization error for evaluation model [24]. Therefore, OOB is used to evaluate the performance of RF with and without data dimension reduction.…”

Section: ) Comparison Of Pca-rf Without Dimension Reductionmentioning

confidence: 99%

“…[23] proposes a short-term wind power forecasting model based on two-stage feature selection and random forest algorithm to overcome the problems of local optimum and overfitting in the training stage of the model, which has achieved good results in accuracy, efficiency, and adaptability. In reference [24], Random Forest is used for change detection of high-resolution remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

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A Solution for Liquor Recognition Based on PCA-RF and Laser Induced Fluorescence

Song

Huang

2021

IEEE Access

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There are many brands and types of liquor in China. Yet, some merchants use low-quality liquor for high-quality sales, which contains methanol that is harmful to humans. Therefore, the identification of fake liquor is of great significance to human health and brand protection. This paper presents a laser-induced fluorescence (which has very high sensitivity and rapidity) recognition liquor. Specifically, it first sets up a LIF spectrum acquisition system for liquor in the laboratory, uses Principal Component Analysis (PCA) to reduce the dimension of LIF spectral data, and then collects the dimension reduction data for Random Forest (RF) identification. The results show that the average recognition accuracy of RF combined with PCA for liquor is as high as 99.80%. This paper combines PCA-RF with LIF to accurately identify different brands and types of liquor, which provides a new method for the recognition of fake liquor.

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Section: ) Comparison Of Pca-rf Without Dimension Reductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Solution for Liquor Recognition Based on PCA-RF and Laser Induced Fluorescence

Song

Huang

2021

IEEE Access

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“…RF is a machine learning algorithm that combines decision tree and Bagging idea [46]- [48]. RF uses multiple samples to construct different decision tree models, in which each decision tree is forecasted separately.…”

Section: Random Forestmentioning

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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“…Among them, short‐term wind speed prediction means predicting wind speed values for the next 1 to 48 hours. Effective short‐term wind speed prediction has important application value for the wind power industry …”

Section: Introductionmentioning

confidence: 99%

A prediction approach using ensemble empirical mode decomposition‐permutation entropy and regularized extreme learning machine for short‐term wind speed

Tian

Wang

2019

Wind Energy

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Accurate prediction of short-term wind speed is of great significance to the operation and maintenance of wind farms, the optimal scheduling of turbines, and the safe and stable operation of power grids. A prediction approach for short-term wind speed using ensemble empirical mode decomposition-permutation entropy and regularized extreme learning machine is proposed. Firstly, wind speed time series is decomposed into several components with different frequency by ensemble empirical mode decomposition, which can reduce the non-stationarity of the original time series. The permutation entropy value for each component is used to analyze its complexity. The components can be recombined to obtain a set of new subsequences. Then, different prediction models based on regularized extreme learning machine are used to predict each subsequence. Fivefold cross validation is used to improve the reliability of the regularized extreme learning machine model. Finally, the predicted value of each subsequence is superimposed to obtain the final predictive result. Ten minutes, 30 minutes, and 1 hour short-term wind speed data from wind farms in Liaoning Province, China, are used for conducting experiments. The experimental results indicate that the values of the root mean square error of the developed prediction approach utilizing 10 minutes, 30 minutes, and 1 hour interval data are 0.5629, 0.4473, and 0.5697; mean absolute error are 0.4427, 3.0701, and 0.4897; mean absolute percentile error are 4.1456%, 16.8166%, and 6.8166%; relative root mean square are 0.0505, 0.2997, and 0.2609; square sum error are 55.5263, 59.6347, and 64.9154; and the Theil inequality coefficient are 0.0235, 0.0808, and 0.0625, which are much lower than those of the comparison methods. The values of the R square of the developed prediction approach utilizing 10 minutes, 30 minutes, and 1 hour interval data are 0.9363, 0.9161, and 0.9472, and the index of agreement are 0.9994, 0.9925, and 0.9894, whichare higher than those of the comparison methods. The Pearson's test results show that the association strength between the actual value and the predicted values of the proposed approach is stronger. Also, the proposed prediction approach in this paper has higher reliability under the same confidence level. The effectiveness of the proposed prediction approach for short-term wind speed is verified.

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An improved random forest model of short‐term wind‐power forecasting to enhance accuracy, efficiency, and robustness

Cited by 52 publications

References 30 publications

A Solution for Liquor Recognition Based on PCA-RF and Laser Induced Fluorescence

A Solution for Liquor Recognition Based on PCA-RF and Laser Induced Fluorescence

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

A prediction approach using ensemble empirical mode decomposition‐permutation entropy and regularized extreme learning machine for short‐term wind speed

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