應用多元迴歸分析及無母數檢定於政府機關能耗分析

Analysis of Energy Consumption of Government Using Multiple Regression Analysis and Nonparametric Method

陳宏瑋(Hong- Wei Chen)

指導教授 ： 李文興

國立臺北科技大學/機電學院/能源與冷凍空調工程系碩士班/碩士(2013年)

https://doi.org/10.6841/NTUT.2013.00257

摘要

本研究主要針對全台灣政府機關業務類759家(鄉鎮市區之戶政事務所、地政事務所、公所)進行耗能影響因子分析，找出最具有顯著性之耗能因子，考量耗能影響因子進行政府機關適當分群分析及擬定各分群單位之用電指標EUI值。
本研究首先根據100年政府機關能源使用填報資料，運用多元迴歸分析後向選取法分析耗能影響因子，分析結果顯示員工密度(員工數/m2)為最具顯著性之耗能影響因子，並以此因子建立用電指標評估迴歸方程式。進一步將該機關類別根據員工密度分為三類，低員工密度群、中員工密度群及高員工密度群，再將每一員工密度群之中位數代入機關類別用電指標評估迴歸方程式，進而根據不同員工密度群擬定用電指標EUI值。
本研究進一步運用無母數檢定方法，根據員工密度迴歸係數判定各機關之分類歸屬。無母數分析結果顯示以機關類別分群，戶政及公所歸為一類，地政事務所歸為一類，2類分群員工密度迴歸係數具有顯著差異。再根據機關類別戶政及公所、地政2類分群後，擬定低、中、高員工密度群之用電指標評估EUI值。

關鍵字

多元迴歸分析法 ； 無母數檢定 ； EUI

並列摘要

This research is focusing on 759 business class government agencies such as Townships of Household Registration Office, Land Officem, Township Office in Taiwan, analyzes its energy consumption influence factor. Identifying the most significant energy consumption influence factor and draw up the electricity indicators EUI value for organ and unit according to it. Cooperating with government, grouping these agencies into appropriate type and probe the relationship of factor after grouped.
In this research, the data is based on 2011 of energy usage in government agencies. Analyzing energy consumption influence factor by using backward method of multiple regressions and the result shows that the occupant density (Occupant/m2) is the most obviously. Establish electricity using indicators regression equation which is based on multiple regressions analysis. The authorities further divided into three categories based on their occupant density, low occupant density group, medium occupant density group and high occupant density group. Then take the median of the group into electricity using indicators regression equation, and then draw up the electricity indicators EUI value depending on the density.
The research also uses nonparametric method. The determination of classifying the organs vested is based on the occupant density regression coefficient. The result of nonparametric method shows that there are significant differences between Household Registration Office and Land Office. The result of nonparametric method also shows that Township Office and Household Registration are same group. Than according to these two groups, drew up the low, medium and high occupant density electricity using indicators to estimate the EUI value.

並列關鍵字

Multiple Regression Analysis ； Nonparametric Method ； EUI

參考文獻

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