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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/109061

    Title: Daily Peak Load Forecasting Based on Fast K-medoids Clustering, GARCH Error Correction and SVM Model
    Authors: Song, Zongyun;Niu, Dongxiao;Xiao, Xinli;Wu, Han
    Keywords: Daily Peak Load Forecasting;FKM;SVM;GARCH;Error Correction
    Date: 2016-09
    Issue Date: 2017-01-03 09:06:54 (UTC+8)
    Publisher: 淡江大學出版中心
    Abstract: Safe and economic operation of power system is based on load forecasting, and how to increase
    forecasting accuracy is the premise of power dispatching and economic analysis. Present paper
    establishes SVM (support vector machine) forecasting model based on fast K-medoids clustering
    algorithm and data accumulative pre-processing. FKM (fast K-medoids clustering algorithm) is
    applied to extract similar days by dividing all samples into k clusters, and respective forecasting of k
    clusters can realize the forecasting of a whole object. Before inputting the data into SVM system, the
    original data is preprocessed by accumulation to weaken the irregularity disturbance and strengthen
    sequence regularity. Due to existing unexplained component in forecasting error, GARCH
    (generalized autoregressive conditional heteroskedasticity) model is employed to forecast the error
    with non-white noise. According to its results, error correction is applied to the forecasted daily peak
    load. The forecasting effect of the proposed model is compared with other models in the given
    example, which verifies that SVM model based on fast K-medoids clustering algorithm and GARCH
    model has the characteristic of effectiveness, superiority and universality. The accuracy of daily peak
    load forecasting is enhanced significantly
    Relation: Journal of Applied Science and Engineering 19(3), pp.249-258
    DOI: 10.6180/jase.2016.19.3.02
    Appears in Collections:[淡江理工學刊] 第19卷第3期

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