Modified Approach of Manufacturer’s Power Curve Based on Improved Bins and K-Means++ Clustering

Fang, Yuan; Wang, Yibo; Liu, Chuang; Cai, Guowei

doi:10.3390/s22218133

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…There is extensive literature suggesting different estimation methods for the power curve. Methods for this estimation are mainly regressions [16,17,11]. Other methods that are used are naive approaches that are based on the most recent information available [4], for the purpose of this work that was considered as the closest turbine that it is possible to get information from.…”

Section: Introductionmentioning

confidence: 99%

Wind Power Prediction using Multi-Task Gaussian Process Regression with Lagged Inputs

Ávila,

Verstraeten,

Vratsinis

et al. 2023

J. Phys.: Conf. Ser.

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Wind is a renewable energy source that has become more important in recent years. Wind turbines are equipped with a SCADA system, which allows for remote supervision of the wind farm. SCADA systems are customarily used to provide data averaged every 10 minutes. Nevertheless, recent literature suggests that more insights could be extracted with a higher granularity of data. In this work, a naive methodology based on Multi-Task Gaussian Process Regression is presented, in order to show how spatiotemporal modeling benefits power estimation. Using sparsity properties a model for possible power prediction is proposed. The model proposed performs better than the power curves provided by the manufacturer.

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Section: Introductionmentioning

confidence: 99%

Wind Power Prediction using Multi-Task Gaussian Process Regression with Lagged Inputs

Ávila,

Verstraeten,

Vratsinis

et al. 2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

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Bert-CK: A study of user profile classification based on Bert and CK-means+ fusion

Qian

Shao

Zhang

et al. 2023

IFS

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In traditional user portrait construction methods, static word vectors can extract only shallow semantic representations, which cannot manage word polysemy. Moreover, the common clustering algorithm K-means has the problems of initial K values and unstable initial centroid selection. A Bert-CK model based on Bert and CK-means+ is proposed. First, Bert is used to extract semantic and syntactic text features at various levels, and word vectors and sentence vectors are obtained according to the context. Then, the CK-means+ algorithm is improved based on canopy and mean calculation. Next, the K value and initial centroid are determined. The sentence vectors are input to CK-means+ to obtain user classification and topic features. Finally, semantic features and topic features are fused and classified. CK-means+ is evaluated on the Sogou user portrait dataset. The experimental results verify that Bert-CK is better than the baseline model.

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Modified Approach of Manufacturer’s Power Curve Based on Improved Bins and K-Means++ Clustering

Cited by 2 publications

References 17 publications

Wind Power Prediction using Multi-Task Gaussian Process Regression with Lagged Inputs

Wind Power Prediction using Multi-Task Gaussian Process Regression with Lagged Inputs

Bert-CK: A study of user profile classification based on Bert and CK-means+ fusion

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