Split incremental clustering algorithm of mixed data stream

Gorrab, Siwar; Ben Rejab, Fahmi; Nouira, Kaouther

doi:10.1007/s13748-024-00316-1

Cited by 1 publication

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In short, the goal of clustering is to divide the dataset into multiple categories according to some criteria (such as the closest distance between elements, the farthest distance, or the average distance), so that the characteristics of data points within the same category are as consistent as possible, while the data points between different categories show greater differences. For example, K-means [11], density clustering [12][13][14], hierarchical clustering [15,16], spectral clustering [17][18][19], and incremental clustering [20][21][22] can effectively classify wind turbines to optimize operation and maintenance strategies and improve energy output efficiency. ST-TRACLUS was proposed in reference [23], which is a novel spatio-temporal clustering algorithm, which enhances the DBSCAN framework through spatial and temporal analysis to identify similarities in trajectory data.…”

Section: Related Workmentioning

confidence: 99%

Research on the Short-Term Prediction of Offshore Wind Power Based on Unit Classification

Zhang,

Liu,

Yan

2024

Electronics

View full text Add to dashboard Cite

The traditional power prediction methods cannot fully take into account the differences and similarities between units. In the face of the complex and changeable sea climate, the strong coupling effect of atmospheric circulation, ocean current movement, and wave fluctuation, the characteristics of wind processes under different incoming currents and different weather are very different, and the spatio-temporal correlation law of offshore wind processes is highly complex, which leads to traditional power prediction not being able to accurately predict the short-term power of offshore wind farms. Therefore, aiming at the characteristics and complexity of offshore wind power, this paper proposes an innovative short-term power prediction method for offshore wind farms based on a Gaussian mixture model (GMM). This method considers the correlation between units according to the characteristics of the measured data of units, and it divides units with high correlation into a category. The Bayesian information criterion (BIC) and contour coefficient method (SC) were used to obtain the optimal number of groups. The average intra-group correlation coefficient (AICC) was used to evaluate the reliability of measurements for the same quantized feature to select the representative units for each classification. Practical examples show that the short-term power prediction accuracy of the model after unit classification is 2.12% and 1.1% higher than that without group processing, and the mean square error and average absolute error of the short-term power prediction accuracy are reduced, respectively, which provides a basis for the optimization of prediction accuracy and economic operation of offshore wind farms.

show abstract