Zeyu Wu¹, Bo Sun^1,2, Qiang Feng^2,*, Zili Wang¹, Junlin Pan¹

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 527-554, 2023, DOI:10.32604/cmes.2023.027124

Abstract Due to the high inherent uncertainty of renewable energy, probabilistic day-ahead wind power forecasting is crucial for modeling and controlling the uncertainty of renewable energy smart grids in smart cities. However, the accuracy and reliability of high-resolution day-ahead wind power forecasting are constrained by unreliable local weather prediction and incomplete power generation data. This article proposes a physics-informed artificial intelligence (AI) surrogates method to augment the incomplete dataset and quantify its uncertainty to improve wind power forecasting performance. The incomplete dataset, built with numerical weather prediction data, historical wind power generation, and weather factors data, is augmented based on generative… More > Graphic Abstract

Yutian Hong^1,*, Yuping Yan²

Energy Engineering, Vol.120, No.1, pp. 245-261, 2023, DOI:10.32604/ee.2022.022877

Abstract With the rapid development of the economy, the scale of the power grid is expanding. The number of power equipment that constitutes the power grid has been very large, which makes the state data of power equipment grow explosively. These multi-source heterogeneous data have data differences, which lead to data variation in the process of transmission and preservation, thus forming the bad information of incomplete data. Therefore, the research on data integrity has become an urgent task. This paper is based on the characteristics of random chance and the Spatio-temporal difference of the system. According to the characteristics and data… More >

Lingyun Xiang^1,2, Guohan Zhao¹, Qian Li³, Gwang-Jun Kim^4,*, Osama Alfarraj⁵, Amr Tolba^5,6

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 267-284, 2021, DOI:10.32604/cmc.2021.013488

Abstract Multiple kernel clustering is an unsupervised data analysis method that has been used in various scenarios where data is easy to be collected but hard to be labeled. However, multiple kernel clustering for incomplete data is a critical yet challenging task. Although the existing absent multiple kernel clustering methods have achieved remarkable performance on this task, they may fail when data has a high value-missing rate, and they may easily fall into a local optimum. To address these problems, in this paper, we propose an absent multiple kernel clustering (AMKC) method on incomplete data. The AMKC method first clusters the… More >