English  |  正體中文  |  简体中文  |  Items with full text/Total items : 90570/105786 (86%)
Visitors : 16335580      Online Users : 293
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    ASIA unversity > 管理學院 > 經營管理學系  > 期刊論文 >  Item 310904400/111643

    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.
    Appears in Collections:[經營管理學系 ] 期刊論文

    Files in This Item:

    File SizeFormat

    All items in ASIAIR are protected by copyright, with all rights reserved.

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback