Short-Term Wind Power Prediction Based On Particle Swarm Optimization-Extreme Learning Machine Model Combined With Adaboost Algorithm

An, Guoqing; Jiang, Ziyao; Cao, Xin; Liang, Yufei; Zhao, Yuyang; Li, Zheng; Dong, Weichao; Sun, Hexu

doi:10.1109/access.2021.3093646

Cited by 66 publications

(33 citation statements)

References 25 publications

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“…In this set of experiments, the proposed SSFE was compared with RF, SVM, KNN, Adaboost [47], LeNet-5 [48], 1DCNN [49], and 2DCNN [50]. Table 5 shows the accuracies related to the OA and Kappa for different methods for the Pavia University data set while maintaining the minimum standard deviation.…”

Section: B Classification Results and Analysismentioning

confidence: 99%

Spectral-Spatial Features Extraction of Hyperspectral Remote Sensing Oil Spill Imagery Based on Convolutional Neural Networks

et al. 2022

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Marine oil spills accidents has caused a large amount of crude oil to leak into the marine environment and threaten marine ecological environment. Hyperspectral remote sensing images (HRSI) technology can quickly and accurately identify oil film of different thickness on marine surface. In order to overcome the traditional spectrum analysis method and space extraction method of long time sampling, calculation, analysis and other shortcomings. On account of the advantages of the spectral and spatial information in the field of HRSI classification, a spectral-spatial features extraction (SSFE) method based convolutional neural networks (CNNs) was proposed to analyse oil spills. In this way, one and two dimensional models based on convolutional neural networks (1D-CNN,2D-CNN) have been introduced as the spectral and spatial features extractor. When extracting spatial features, double-two convolution layers are connected to increasing nonlinearity and reduce the number of parameters. Furthermore, in order to address overfitting and imbalance samples, L2 regularization, class_weight and drouput is added to classes data modeling. More importantly, principal component analysis (PCA) is applied to data dimension reduction, 1D-CNN and 2D-CNN is combined into a unified model further extract the joint spatial-spectral features. To evaluate the effectiveness of the proposed approach, three hyperspectral datasets were utilized, including: University of Pavia dataset, oil spill area 1, oil spill area 2. Experimental results reveal that the proposed method have a very satisfactory performance and better distinguish oil spills.INDEX TERMS Marine oil spills, hyperspectral remote sensing images (HRSI), convolutional neural networks (CNNs), spectral-spatial features extraction (SSFE).

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Section: B Classification Results and Analysismentioning

confidence: 99%

Spectral-Spatial Features Extraction of Hyperspectral Remote Sensing Oil Spill Imagery Based on Convolutional Neural Networks

et al. 2022

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“…x k x k n (12) where ( ) is the actual value and ( ) is the predicted value. The iterative curves of the four swarm intelligence models are shown in Figure 9.…”

Section: Msementioning

confidence: 99%

“…It has been widely used in the wind power industry and other projects that require prediction [11]. An et al used the particle swarm optimization algorithm (PSO) to optimize the extreme learning machine (ELM) and combined them with the Adaboost integrated learning model to make a short-term prediction of wind power [12]. However, the model takes wind speed and direction as input and wind power as output.…”

Section: Introductionmentioning

confidence: 99%

Ultra Short-Term Wind Power Forecasting Based on Sparrow Search Algorithm Optimization Deep Extreme Learning Machine

Jiang

Chen

et al. 2021

Sustainability

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Improving the accuracy of wind power forecasting is an important measure to deal with the uncertainty and volatility of wind power. Wind speed and wind direction are the most important factors affecting the power generation of wind turbines. In this paper, we propose a wind power forecasting method that combines the sparrow search algorithm (SSA) with the deep extreme learning machine (DELM). Based on the DELM model, the length of the time series’ influence on the performance of the neural network is validated through the comparison of the forecast error indexes, and the optimal time series length of the wind power is determined. The sparrow search algorithm is used to optimize its parameters to solve the problem of random changes in model input weights and thresholds. The proposed SSA-DELM model is validated using the measured data of a certain wind turbine, and various forecasting indexes are compared with several current wind power forecasting methods. The experimental results show that the proposed model has better performance in ultra-short-term wind power forecasting, and its coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE) are 0.927, 69.803, and 115.446, respectively.

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“…Manual parameter tuning is inefficient and relies on prior knowledge. Therefore, some research adopted intelligent optimization algorithms to optimize prediction model parameters automatically [24][25][26]. However, the accuracy of the prediction model was taken as the only optimization objective in these researches.…”

Section: Introductionmentioning

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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Short-Term Wind Power Prediction Based On Particle Swarm Optimization-Extreme Learning Machine Model Combined With Adaboost Algorithm

Cited by 66 publications

References 25 publications

Spectral-Spatial Features Extraction of Hyperspectral Remote Sensing Oil Spill Imagery Based on Convolutional Neural Networks

Spectral-Spatial Features Extraction of Hyperspectral Remote Sensing Oil Spill Imagery Based on Convolutional Neural Networks

Ultra Short-Term Wind Power Forecasting Based on Sparrow Search Algorithm Optimization Deep Extreme Learning Machine

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

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