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  • Open Access


    Physics-Informed AI Surrogates for Day-Ahead Wind Power Probabilistic Forecasting with Incomplete Data for Smart Grid in Smart Cities

    Zeyu Wu1, Bo Sun1,2, Qiang Feng2,*, 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,… More > Graphic Abstract

    Physics-Informed AI Surrogates for Day-Ahead Wind Power Probabilistic Forecasting with Incomplete Data for Smart Grid in Smart Cities

  • Open Access


    Power Incomplete Data Clustering Based on Fuzzy Fusion Algorithm

    Yutian Hong1,*, 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… More >

  • Open Access


    A Fast and Effective Multiple Kernel Clustering Method on Incomplete Data

    Lingyun Xiang1,2, Guohan Zhao1, Qian Li3, Gwang-Jun Kim4,*, Osama Alfarraj5, Amr Tolba5,6

    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… More >

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