ASIA unversity:Item 310904400/111643
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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/111643


    Title: Enhanced Gaussian Process Mixture Model for Short-Term Electric Load Forecasting
    Authors: Li, Ling-Ling;Li, Ling-Ling;Sun, Jin;Sun, Jin;Ching-Hsin, W;Wang, Ching-Hsin;Zhou, Ya-Tong;Zhou, Ya-Tong;林國平;Lin, Kuo-Ping
    Contributors: 管理學院經營管理學系
    Date: 2018-10
    Issue Date: 2018-12-24 16:21:13 (UTC+8)
    Abstract: This research used a hard-cut iterative training algorithm to improve a Gaussian process mixture (GPM) model. Our enhanced GPM (EGPM) concisely estimates distribution parameters to the greatest extent possible. GPM models are powerful tools for data presentation and forecasting owing to their linear mix of multiple Gaussian process (GP) models. The hidden posterior probability distribution variables in the GPM model, which are based on the hard-cut algorithm, are 0 and 1, respectively, which can simplify the training process and reduce calculation requirements by training each GP via a maximum likelihood estimation method. The EGPM model is then used for a short-term electric load forecasting problem and compared with various forecasting models. First, the EGPM results are compared with those of two previous GPM training algorithms: the variational and leave-one-out cross validation (LOOCV) algorithms. The experimental results indicate that the EGPM model can accurately and more reliably forecast electric loads. The GP, support vector machine, and radial basis function network are also assessed for their ability to solve the short-term electric load forecasting problem. The empirical results indicate that the performance of the proposed EGPM is superior to that of the other methods in terms of forecasting accuracy.
    Relation: INFORMATION SCIENCES
    Appears in Collections:[Department of Business Administration] Journal Article

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