English  |  正體中文  |  简体中文  |  Items with full text/Total items : 25444/26039 (98%)
Visitors : 6413753      Online Users : 420
RC Version 7.0 © 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


    Please use this identifier to cite or link to this item: http://ir.lib.ksu.edu.tw/handle/987654321/20380


    Title: Construct Analytical Models to Support Renewable Energy Policies Using Data Mining Techniques
    Authors: Amy, J.C.Trappey
    Charles V. Trappey
    Danny Y.C. Wang
    Ruby H.W. Lu
    Jerry J.R. Ou
    Contributor: Department of Industrial Engineering and Engineering Management, National Tsing Hua University
    Department of Management Science, National Chaio Tung University
    Department of Business Administration, Southern Taiwan University
    Keywords: Renewable Energy
    Data Mining
    Clustering
    Self-Organizing Map
    Date: 2013-11-02
    Issue Date: 2013-11-14 15:26:50 (UTC+8)
    Abstract: When a large percentage of energy (>90%) is still generated by fossil fuel, carbon dioxide emission, the greenhouse effect, and subsequent environmental damages remain to be major problems for many countries. Therefore, renewable, sustainable, and economically viable energy sources are sought after as alternatives to fossil fuels. The advantages of using renewable energy are that it will never run out, emits nearly no carbon dioxide, and has little negative effect on the eco-system. However, the facility and installation cost for generating renewable energy is much higher than the cost of fossil fuel generated energy. Thus, governments should form effective policies, regulations, and incentive programs to promote the usage of renewable energy. Renewable energy can be classified into different categories, e.g., offshore and onshore wind power, photovoltaic solar, and geothermal. The policies used for promoting specific categories vary significantly. These policies consist of the policy goals, regulations, taxations, incentives and promotional schemes. The purpose of this study is to apply data mining techniques to analyze types of renewable energies and their attributes with respect to economic factors, energy resource and supply, and environmental effects. The study provides a scientific outlook to help governments plan their renewable energy policies. In our specific case study, the data from Taiwan’s renewable energy statistics are related to photovoltaic, wind farms, ocean thermal energy conversion, geothermal, hydro power, and solid wastes. The research has two major results and findings, i.e., 1. Develop analytical models for the decision support of renewable energy policy using intelligent data mining techniques.
    2. Four clusters of renewable energy sources in Taiwan are identified for the future planning of suitable promotional schemes.
    Appears in Collections:[機械工程系所] Automation 2013- The 12th International Conference on Automation Technology

    Files in This Item:

    File Description SizeFormat
    L002.pdf13KbAdobe PDF191View/Open
    Renewable_FULL_0002.pdf363KbAdobe PDF0View/Open


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


    本網站之所有圖文內容授權為崑山科技大學圖書資訊館所有,請勿任意轉載或擷取使用。
    ©Kun Shan University Library and Information Center
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback