跳到主要內容

簡易檢索 / 詳目顯示

研究生: 林子婷
Tzu-Ting Lin
論文名稱: 氣候因素、所得和人口特徵對電力需求之影響-台北市為例
Climate factors, Income and Demographic causes of electricity demand in Taipei City
指導教授: 劉錦龍
Jin-Long Liu
口試委員:
學位類別: 碩士
Master
系所名稱: 管理學院 - 產業經濟研究所
Graduate Institute of Industrial Economics
論文出版年: 2017
畢業學年度: 105
語文別: 中文
論文頁數: 52
中文關鍵詞: 電力需求縱橫資料固定效果模型
外文關鍵詞: Electricity Demand, Panel Data, Fixed effect model
相關次數: 點閱:11下載:0
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 日新月異的時代,能源的供需問題一直是我們需要關注,電力對民眾來說是不可或缺的,也是經濟發展的動力,因此本研究加入地理資訊系統(QGIS)測量氣候因素、所得和人口特徵變數對住宅用電量作探討。研究範圍為2014年至2016年夏季(6、7、8、9月)台北市村里的縱橫資料(Panel Data),使用最小平方法(Ordinary Least Squares)及固定效果模型(Fixed effect model)進行分析。
    研究結果顯示:(1)熱天日數是主要影響台北市村里用電量因素。(2)以平均每戶每月所得固定效果之實證模型中來探討所得為顯著負向影響。(3)當解釋變數加入人口特徵變數探討時,每人每月平均所得條件下,人口密度為負向不顯著,而每戶每月平均所得條件下,人口密度為負向顯著,表示有規模經濟,而所得為每戶每月平均所得解釋能力比每人每月平均所得解釋能力好。(4)村里戶數為顯著正向影響,表示台北市住宅結構改變,以單身居住者居多。(5)人口特徵變數中65歲以上中老年人顯著正向影響用電量。


    With the progress of the times, we need to focus on energy supply and demand problem. Electricity is indispensable of modern life, but also promotes economic development. As a result, use geographic information systems (QGIS) to measure climate factors, income and demographic variables on residential electricity consumption as an investigation. For this purpose, we use Ordinary Least Squares and Fixed effect model to estimate demand equation with this analysis of panel data of village during the period 2014-2016 in Taipei City.

    The empirical results show that:(1)The Heating Degree Days is significantly related to electricity consumption in Taipei City.(2) Average monthly income per household in the empirical model of fixed effects results for income is a significantly negative impact.(3)When joins the demographic characteristic variable, under the condition of average monthly income per person, population density is negative and insignificant. While the average monthly income per household conditions, population density is negative and significant, indicating the economies of scale. (4) The positive and significant influence on the number of household indicates the changes of residential structure-mostly live singles (5) Elderly above 65 years old is positively and significantly related to electricity consumption.

    圖目錄 II 表目錄 III 第一章 緒論 1 1.1研究動機與目的 1 1.2研究方法 4 1.3研究架構 4 第二章 研究背景 5 2.1 智慧節電計畫概況 5 2.2台北市行政區和用電量狀況 8 第三章 文獻回顧 10 3.1 探討省份及城鎮地區對用電量的影響 10 3.2 探討家庭用戶對用電量的影響 12 第四章 研究方法與資料 14 4.1實證模型 14 4.2資料來源與變數解釋 16 4.3敘述統計 18 第五章 實證結果 22 5.1 實證結果分析 22 5.2固定效果模型分析 28 5.3小結 34 第六章 結論與建議 36 6.1結論 36 6.2研究限制與建議 36 參考文獻 37 附錄一、2014年平均每人夏季每月用電量村里圖 39 附錄二、2015年平均每人夏季每月用電量村里圖 40 附錄三、2016年平均每人夏季每月用電量村里圖 41 附錄四、平均每人夏季每月用電量變化村里圖 42

    1.Tiwari, Piyush. 2000.“Architectural, Demographic, and Economic Causes of Electricity Consumption in Bombay.” Journal of Policy Modeling,22(1):81-98.
    2. Filippini, Massimo,and Shonali Pachauri.2004.“Elasticities of electricity demand in urban Indian households.” Energy Policy ,32:429–436.
    3. Wiesmann, Daniel, Inês Lima Azevedo, Paulo Ferrão,and John E. Fernández.2011.“Residential electricity consumption in Portugal: Findings from top-down and bottom-up models.”Energy Policy,39(5): 2772–2779.
    4. Bernard, Jean-Thomas, Denis Bolduc, and Nadège-Désirée Yameogo.2011“A pseudo-panel data model of household electricity demand.”Resource and Energy Economics,33(1): 315–325.
    5. Hamilton, Lawrence C., Daniel M. White, Richard B. Lammers, and Greta Myerchin.2012.“Population, climate, and electricity use in the Arctic integrated analysis of Alaska community data.”Population and Environment,33(4):269-283.
    6. Blázquez , Leticia ,Nina Boogen,and Massimo Filippini.2013.“Residential electricity demand in Spain: New empirical evidence using aggregate data.”Energy Economics,36:648-657.
    7. Blázquez Gomez, Leticia M., Massimo Filippini, and Fabian Heimsch.2013.“Regional impact of changes in disposable income on Spanish electricity demand : A spatial econometric analysis.” Energy Economics,40:58-66.
    8. Romero-Jordán,Desiderio, Cristina Peñasco,and Pablo del Río.2014.“Analysing the determinants of household electricity demand in Spain. An econometric study.”International Journal of Electrical Power & Energy Systems,63:950-961.
    9. Fan, H., I.F. MacGill,and A.B. Sproul.2015. “Statistical analysis of driving factors of residential energy demand in the greater Sydney region, Australia.” Energy and Buildings,105:9-25.
    10. He, Xiaoping, and David Reiner.2016. “Electricity demand and basic needs: Empirical evidence from China's households.”Energy Policy, 90:212–221.
    11. Salari ,Mahmoud, and Roxana J.Javid.2016.“Residential energy demand in the United States: Analysis using static and dynamic approaches.”Energy Policy,98:637-649.

    QR CODE
    :::