Accurate wind power forecasting plays an increasingly significant role in power grid normal operation with large-scale wind energy. The precise and stable forecasting of wind power with short computational time is sti...
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Accurate wind power forecasting plays an increasingly significant role in power grid normal operation with large-scale wind energy. The precise and stable forecasting of wind power with short computational time is still a challenge owing to various uncertainty factors. This study proposes a hybrid model based on a data prepossessing strategy, a modified bayesianoptimization algorithm, and the gradient boosted regression trees approach. More specifically, the powerful information mining ability of maximum information coefficient is used to select the important input features, and the modified bayesianoptimization algorithm is introduced to optimize the hyperparameters of the gradient boosted regression trees to acquire more satisfactory forecasting precision and computation cost. Datasets from a Chinese wind farm are used in case studies to analyze the prediction accuracy, stability, and computation efficiency of the proposed model. The point forecasting and multi-step forecasting results reveal that the performance of the hybrid forecasting model positively exceeds all the contrasted models. The developed model is extremely useful for enhancing prediction precision and is a reasonable and valid tool for online prediction with increasing data.
Within this paper we proposed a new method named BayesACO, to improve the convolutional neural network based on neural architecture search with hyperparametersoptimization. At its essence Bayes ACO in first side uses...
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ISBN:
(纸本)9783030863340;9783030863333
Within this paper we proposed a new method named BayesACO, to improve the convolutional neural network based on neural architecture search with hyperparametersoptimization. At its essence Bayes ACO in first side uses Ant Colony optimization (ACO) to generate the best neural architecture. In other side, it uses bayesian hyperparameters optimization to select the best hyperparameters. We applied this method on Mnist and FashionMnist datasets. Our proposed method proven competitive results with other methods of convolutional neural network optimization.
The improvement of wind power prediction accuracy is beneficial to the effective utilization of wind energy. An improved XGBoost algorithm via bayesian hyperparameter optimization (BH-XGBoost method) was proposed in t...
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The improvement of wind power prediction accuracy is beneficial to the effective utilization of wind energy. An improved XGBoost algorithm via bayesian hyperparameter optimization (BH-XGBoost method) was proposed in this article, which is employed to forecast the short-term wind power for wind farms. Compared to the XGBoost, SVM, KELM, and LSTM, the results indicate that BH-XGBoost outperforms other methods in all the cases. The BH-XGBoost method could yield a more minor estimated error than the other methods, especially in the cases of wind ramp events caused by extreme weather conditions and low wind speed range. The comparison results led to the recommendation that the BH-XGBoost method is an effective method to forecast the short-term wind power for wind farms.
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