* ᖒɁšݡ⦺Ʊ u-CityŖ⦺ŝ ᕾᔍŝᱶ ([email protected])
** ⦽ǎ☁ḡᵝ┾Ŗᔍ ࠥ⪹Ğᔍᨦ ᨱթḡᔍᨦᇡ ŝᰆ ([email protected])
Received March 25 2013, Revised April 16 2013, Accepted April 19 2013
Copyright ⵑ 2013 by the Korean Society of Civil Engineers
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0)
ǣǣȀȀǤǤȀͳͲǤͳʹͷʹȀ ǤʹͲͳ͵Ǥ͵͵Ǥ͵Ǥͳʹʹͷ
Ǥ ǤǤ
Ⲏቧሾ㐦⢚⡢㜚ⴂ#㰚Ⱨ㬚#ኳᦗ⺺㚛ⴖ#ᦗⷆᏮ#⮎ᛆ⽾#❊␂㣦㛲#⍂⛛#
ச ȵกজ ȵଲܛฅ ȵࢮ็
Shin, Juho*, Kim Hongseok**, Lee Donghwan***, Park seunghee****
A Study on the pattern of energy consumption of apartment in winter with Automatic Meter Reading Systems
ABSTRACT
According to the importance of greenhouse gas emissions, it grows day by day, the goverment is promoting to prepare the specific policy implementation to enhance building energy-saving design standars as the development agenda. In this study, the statistical analysis was performed by Descriptive statistics, Regression analysis, and Hypothesis testing to collect to generate and storage energy usage data in real time to settle parameter setting to affect energy consumption under energy-guzzling apartment not single building. This study is expected to be utilized as the basis for the optimum energy-saving design of the future of the building or facility energy costs rise and the demand for energy-efficient and stable management.
Key words : Automatic Meter Reading Systems, Apartment, The pattern of energy consumption, Energy use plan
Ⅹಾ
ḡᗮᱢᯙ⪵ᕾᩑഭ᮹ᔍᬊᮝಽ᪉ᝅaᜅ᮹႑⇽పᯕ᷾aࡹᨕḡǍ᪉ӽ⪵⩥ᔢᯕၽᔾ⧉ᨱᯕෝสᮝಅ۵ǎԕᬡḢᯥᯕ⪽ၽ⦹í
ᱥ}ࡹŁᯩ݅. əᨱෙǎԕ᮹ᨱթḡᱩ᧞ᱶ₦ᝅ⩥ŝšಉ⦹ᩍᨱթḡᗭእᮉᯕ׳ᮡÕྜྷᇡྙᮡᨱթḡq⇶ᰁᰍపᯕᔑᨦ, ᙹᘂ॒݅ෙ
ᇡྙᨱእ⧕aᰆⓑᇡྙᯕ݅. ⬉ŝᱢᯙᨱթḡq⇶᮹↽ᖁ₦ᮝಽ⬉ᮉᱢᯙᨱթḡᱩ᧞ᖅĥၰ↽ᱢ᮹ᨱթḡᙹšญෝ᭥⧕Õྜྷ᮹ᨱթḡ ᔍᬊᨱݡ⦽☖⧊ߑᯕ░a⦥⦹ӹʑ᳕☖ĥߑᯕ░۵ݡᇡᇥŖɪ☖ĥߑᯕ░ෝʑၹᮝಽ⦹Łᯩᨕ⪽ᬊᨱ⦽ĥaᯩ݅. ᯕᨱᅙᩑǍᨱᕽ۵
݉ᯝÕྜྷᮡᦥܩḡอᨱթḡ݅ᗭእǑᯙŖ࠺ᵝ┾ᯕ۵ᇡᇥᱢᯙᙹ݉᮹ᝅeᮝಽᔾᖒᱡᰆࡹ۵ᨱթḡᔍᬊపߑᯕ░ෝᙹḲ⦹Łᨱ թḡᗭእᨱᩢ⨆ᮥᵥᙹᯩ۵ᄡᙹෝᖅᱶ⦹ᩍ☖ĥᇥᕾ⧉ᮝಽ៉⨆⬥Õ⇶ྜྷੱ۵ᖅ᮹ᨱթḡၰእᬊᔢᯕᨧ۵↽ᱢ᮹ᨱթḡᱩ᧞ᖅ ĥ᪡⬉ᮉᱢᯕŁᦩᱶࡽᨱթḡᙹšญෝ᭥⦽ʑⅩᯱഭಽ⪽ᬊࡹʑෝʑݡ⦽݅.
áᔪᨕ ᬱĊá⋉ᜅ▽, ᨱթḡᔍᬊĥ⫮, Ŗ࠺ᵝ┾, ᨱթḡᗭእ➉▕
1. ᕽು
1.1 ֜ଭࢼլࢫࡧୡ
⩥ᰍ ⠪Ɂʑ᪉ ၰ ⧕ᙹ໕ ᔢ, ᩍ్ ʑᔢᰍ⧕ ॒ ᬑญӹ᮹ ʑ⬥ᄡ⪵ ḥ⧪ᗮࠥa ᖙĥ⠪Ɂᮥ ᔢ⫭⧉ᨱ ʑ⬥ᄡ⪵ಽ ᯙ⦽
⪹Ğ ᔍ⫭ Ğᱽᱢᯙ⦝⧕ᨱᱢɚᱢᮝಽݡእ⦹۵äᯕ⦥⦹ᩡᮝ໑, ǎԕᨱᕽ۵ǎ☁ᇥ᮹ᱶ₦ŝᱽಽᱡ┥ᗭךᔪǎ☁ ࠥŖe
᳑ᖒၰÕ⇶ྜྷ᮹᪉ᝅaᜅq⇶ᮥ᭥⦹ᩍ⊽⪹Ğᵝ┾᮹Õᖅʑᵡ, ᝁ ᰍᔾᨱթḡᯕᬊၰÕ⇶ྜྷᨱթḡᱩ᧞ᖅĥʑᵡᮥv⪵⦹۵ ܁҃şց
Table 1. Variable selection and statistical techniques
Independent variable Dependent variable Statistics technique
- Tenant characteristics (number of household members, age) - Weather conditions (temperature, humidity, and wind speed) - Time (month / day, day of week, time)
- Space (top / middle / lowest side / center)
- Interior Building Material(installing corridor windows or not )
Energy consumption - Electricity consumption - Gas consumption
Descriptive statistic Regression analysis Hypothesis testing(t-test)
॒ךᔪᖒᰆǎaᱥఖᮥ ⬉ᮉᱢᯕŁℕĥᱢᮝಽᯕ⧪⦹ʑ᭥⧕
Ǎℕᱢᯙ ᱶ₦ᝅ⩥ ႊᦩॅᯕ ษಉࡹᨕ ⇵ḥࡹŁ ᯩ݅.
ךᔪᖒᰆ᭥ᬱ⫭᮹ǎa᪉ᝅaᜅq⇶༊⢽ᖅᱶႊᦩ(2009)ᨱ
ᱶᇡ۵20ᖙݡᯕᔢ᮹༉ुŖ࠺ᵝ┾ᮡ2017֥ᇡ░۵ᨱթḡᱩ qශᯕ60%ᯕᔢᯙ➉ቭ⦹ᬑᜅ(Passive House)ᙹᵡᮝಽ, 2025
֥ᇡ░۵Õ⇶ྜྷ᮹ᨱթḡ⬉ᮉᮥɚݡ⪵⦹Łᝁᰍᔾᨱթḡෝᔾᔑ
ᯕᬊ⧉ᮝಽ៉, ᩑe Õྜྷᨱᕽ᮹ ᨱթḡᗭእ᪡ ᔾᔑᯕ ࠺ᯝ⦽
ᱽಽᨱթḡᵝ┾Ŗɪᮥ༊⢽ಽ݉ĥᱢᮝಽᨱթḡ᮹ྕᱩqශ᮹
ᔢ⨆᳑ᱶᮥ⇵ḥ⦹Łᯩ݅. əᨱᨱթḡ᮹ྕᱩqශᮥݍᖒ⦹
ʑ᭥⦹ᩍ⊽⪹Ğᵝ┾᮹ᨱթḡᖅĥʑᵡၰÕ⇶ྜྷᨱթḡᱩ᧞ᖅĥ ʑᵡᯕ ᱱ₉ v⪵ࡹŁ ᯩ݅. ᐱอ ᦥܩ ᝁ ᰍᔾᨱթḡ ᯕᬊ
Õ⇶ྜྷᯙ᷾ᱽෝ☖⧕Õ⇶ྜྷ᮹ᱡᨱթḡᔍᬊǍ᳑᪡࠺ᨱᨱթ ḡෝᔾᔑ⦹۵ℕᱽಽᱥ⪹⍽ᨱթḡ⪹ĞᩍÕᄡ⪵॒ᨱ܆࠺ᱢ ᮝಽݡ⦹ࠥಾᝁᰍᔾᨱթḡᅕɪ ⪽ᖒ⪵aᮁࠥࡹŁᯩ݅.
⦽⠙, ᨱթḡᗭእ⊂໕ᨱᕽᱥℕᨱթḡᵲ25%ෝ₉ḡ⦹Łᯩ۵
Õྜྷᇡྙᮡᔑᨦᯕӹᙹᘂᇡྙᅕ݅ᔢݡᱢᮝಽĞᱽᨱၙ⊹۵ᇡݕ
ᮥᵥᯕ໕ᕽᙹšญ᪡ʑᚁ}ၽಽ៉ᨱթḡq⇶ᯕa܆⦽ᰁᰍప ᯕ aᰆ ⓑ ᇡྙᯕʑࠥ ⦹݅. ə్ӹ ┡ ᇡྙᅕ݅ Õྜྷᄥ ŝÑ
ᨱթḡᗭእᯕಆ, ᨱթḡŖɪၰᙹšญෝ᭥⦽DBǍ⇶॒ℕĥ ᱢᯙ☖ĥǍ⇶ᯕၙ⯂⦹݅. ᨱթḡq⇶᮹↽ᖁ₦ᯙ↽ᱢ᮹ᨱթḡ ᱩ᧞ᖅĥ᪡ᨱթḡᙹɪšญෝ᭥⧕ᕽǍℕᱢᯙᨱթḡšಉ☖ĥᯱ
ഭෝ⦥ಽ⦹۵ߑʑ᳕☖ĥᯱഭ۵ݡᇡᇥᙹ☖ĥߑᯕ░a
ᦥܭŖɪ☖ĥߑᯕ░ෝʑၹᮝಽ⦹Łᯩᨕᨱթḡᙹෝšญ⧉
ᨱᯩᨕ⦽ĥaᯩŁ(ǎ☁⧕᧲ᇡ2012), Õ⇶ྜྷ᮹ɝᱲ⦽ᨱթḡᙹ
ᩩ⊂ᮥ᭥⦽ᨱթḡᔍᬊపᇥᕾᨱݡ⦹ᩍᝅᙹ☖ĥߑᯕ░ෝ
⪽ᬊ⦽ ᔍಡa ᇡ᳒⦹݅.
ᯕᨱᅙᩑǍᨱᕽ۵݉ᯝÕྜྷᮡᦥܩḡอᨱթḡ݅ᗭእǑᯙ
Ŗ࠺ᵝ┾ᯕ۵ᇡᇥᱢᯙᙹ݉᮹ᝅeᮝಽᔾᖒ ᱡᰆࡹ۵
ᨱթḡᔍᬊపߑᯕ░ෝᙹḲ⦹Łbb᮹ᇡᄡᙹॅᨱݡ⦽☖ĥᇥ ᕾᮥ ᙹ⧪⦹ᩍ ᨱթḡ ᙹ➉▕ᮥ ᇥᕾ⦹۵ߑ ə ༊ᱢᯕ ᯩ݅.
əđŝಽ⦹ᩍɩ⨆⬥Ŗ࠺ᵝ┾ᖅĥᯱᨱí᯦ᵝᯱa⏭ᱢ⦹Ł
⬉ᮉᱢᯙᵝÑᔾ⪽ᯕa܆⦹ࠥಾᨱթḡၰእᬊᔢᯕᨧ۵↽ᱢ ᮹Õ⇶ᖅĥෝ⧁ᙹᯩࠥಾ⦹Ł, ᨱթḡᔍᨦᯱੱ۵ࠥ}ၽᔍᨦ
ᯱᨱí۵ᨱթḡᙹᇡ⦹ᨱݡ⦽Ǎℕᱢᯙߑᯕ░⪶ᅕෝ☖⦹ᩍ
⬉ᮉᱢᯕŁ ᦩᱶᱢᯙ ᨱթḡ ᙹɪℕĥෝ w⇽ ᙹ ᯩᮥ äᯕ݅.
1.2 ֜ଭ࣐ࢫࢺ࣑
ᅙᩑǍᨱᕽ۵ᯝ֥ᵲᱥℕᨱթḡᗭእ᮹᧞40%ෝ₉ḡ⦹۵
࠺ᱩʑ᮹Ğʑԉ᧲ᵝᨱ᭥⊹⦽⦽ Ŗ࠺ᵝ┾݉ḡෝݡᔢᮝಽ
ᔍᬊప᮹ߑᯕ░ෝ⪽ᬊ⦹ᩍԕ ᇡ⪹Ğ᳑Õ, Ŗeᱢᄡ⪵
ၰÕ⇶ษqᰍᄡ⪵ᨱෙᨱթḡᔍᬊపᄡ⪵పᨱݡ⦽☖ĥᇥᕾ
ᮥᙹ⧪⦹ʑಽ⦽݅. ᯕভᱥʑᔍᬊపᮡ᳑ʑʑၰᱥʑʑʑ᮹
ᔍᬊᱶࠥෝaᜅᔍᬊపᮡӽႊ, ɪ┶, ≉ᔍᨱթḡᯕᬊᱶࠥෝӹ┡
ԙ݅. ᇥᕾჵ᭥۵2010֥12ᬵᇡ░2011֥2ᬵʭḡ᮹ᖙݡᱥᬊᇡ
᭥ᨱᕽᗭࡹ۵ᨱթḡపᨱݡ⦽ᇥᕾᨱ⦽⦹໑, Ŗᬊᇡ᭥ᨱᕽ
ᗭࡹ۵ ᨱթḡప ᇥᕾᮡ ᅙ ᩑǍᨱᕽ ႑ᱽ⦹ᩡ݅. ᅕ݅ ᝍࠥ
ʫᮡ᳑ᔍa a܆⦹ࠥಾ ݉ḡᱥℕᵲ ݉ḡ᮹ ✚ᖒᮥݡ⢽⧁ ᙹ
ᯩ۵ ⢽ᅙᮥ ᖁᱶ⦹ᩍ ᳑ᔍ ᇥᕾᮥ ᙹ⧪⦹ʑಽ ⦹Ł, ᇥᕾ
ᖅᱶ⦽ᄡᙹၰᱢᬊ⦽☖ĥʑჶᮡTable 1ŝz݅. ࢹṙ, ᯕᨱ
ᦿᕽᔍᱥᇥᕾᮝಽᨱթḡᙹᩩ⊂ႊჶುᵲÕྜྷᖒ܆ဍ౩ᯕ ᖹ⥥ಽəఉᨱ᮹⦽↽ᱢ᮹ᨱթḡᗭపŝࠥ}ၽᔍᨦ᮹ᨱթ ḡᔍᬊĥ⫮ᙹพᯝၹᱢᮝಽᔍᬊࡹ۵ᙹᱶ᪉ࠥBIN ᱥᔑญ ჶᮥ ☖⦽ ᨱթḡᙹపᮥ ᔑ⇽⦹ᩡ݅.
2. ᯕುᱢŁₑ
2.1 գՑಅਏਆഗ
ᬱĊá⋉ᜅ▽(Automatic Metering System)ᯕᙹᬊa᮹
݅᧲⦽ᨱթḡᔍᬊపᮥᱥᯱĥపʑෝ⪽ᬊ⦹ᩍᬱĊḡᨱᕽá⋉
ၰšญaa܆⦽ᜅ▽ᮥั⦽݅. Fig. 1ᮡᬱĊá⋉ᜅ▽᮹
}ఖᱢᯙ ĥ☖ࠥᯕ݅.
Ŗ࠺ᵝ┾ᨱ۵ᯝၹᱢᮝಽ2000֥ݡᵲၹᇡ░ᖅĥᨱᱢᬊࡹʑ
᯲⧩۵ߑ, ᬱĊá⋉ᜅ▽᮹ᱢᬊ႑Ğᮡℌṙᖙݡԕᇡᨱᖅ⊹
ࡽĥపʑ᮹á⋉ᨱݡ⦽ᇩ⠙⧉ᮥ}ᖁ⦹Łᯱ᭥⧉ᯕᨩŁ, ࢹṙ
᳦ᱥ᮹šญᗭᯙᬱॅಽ⦹ᩍɩᱥᙹ᳑ᔍ⦹ᩍ⩥ᰆá⋉⦹۵ႊ
ᮥߑᯕ░ʑʑෝ⪽ᬊ⦽ᯱ࠺⪵ᜅ▽ᮝಽ}ᖁ⦹ᩍ⧪ᱶᯙಆԎእ
ෝ สᦥ ᯙಆᬕᬊᨱ ݡ⦽ ᔾᔑᖒᮥ ⨆ᔢ┅Łᯱ ⧉ᯕŁ, ᖬṙ
á⋉ᯱ࠺⪵ෝ☖⦽᯦ᵝᯱ᮹ᔍᔾ⪽ᅕ⪙ၰá⋉ᬱᮥaᰆ⦽ჵᴥ
ᩩႊ॒⏭ᱢ⦹Ł⠙ญ⦽᯦ᵝ⪹Ğᮥ᳑ᖒ⦹ᩍᇥ᧲ᖒᮥᱽŁ⦹ʑ
Table 2. Sample building information
Classification Contents Remarks
Total floor area/Building 5,242.785 m² The number of layers
(Household)
15 stories (75households) Area of supply space
(Area of exclusive use space)
67.76 m² (46.90 m²)
Heating area (41.57 m²) Building azimuth
orientation south-south-west
Number of occupant 200 Members Man:108 Wo :112
Fig. 2. Unit household floor plan and Target building Fig. 1. Automatic Meter Reading Systems schematic
᭥⧉ᯕᨩ݅.(᳑⭹อ2012) á⋉᮹ݡᔢᯕࡹ۵ᨱթḡᬱ᮹᳦ඹ۵
ᱥಆ, ᙹࠥ, aᜅ, ᪉ᙹ, ӽႊ॒ᯕᯩ۵ߑ, Õ⇶ྜྷ᮹ӽႊႊᨱ
á⋉⧕⦹۵ᨱթḡᬱᯕ݅݅. Ḳ݉ᨱթḡŖɪݡᔢḡᩎ ᮹ḡᩎӽႊᮥᔍᬊ⦹۵ᙹᬊa۵ᱥಆ, ᙹࠥ, aᜅ(≉ᔍ), ᪉ᙹ, ӽႊ᮹ᔍᬊపᮥá⋉⦹໑, ə౨ḡᦫŁ}ᄥӽႊᮥᔍᬊ⦹۵ᙹᬊ a۵ ᱥಆ, ᙹࠥ, aᜅᔍᬊపᮥ á⋉⦹í ࡽ݅.
2.2 ٪ॷծฏ
ᨱթḡᔍᬊĥ⫮⩲᮹ᱽࠥ۵ᨱթḡᯕᬊ⧊ญ⪵ჶᨱ᮹Ñݡ☖ಚ ᯕᱶ⦹۵ᯝᱶȽ༉ᯕᔢ᮹ᨱթḡෝᔍᬊ⦹۵ᔍᨦᮥᝅ⦹Ñӹ
ᖅᮥᖅ⊹⦹Łᯱ⧁ Ğᬑᔍᱥᨱᨱթḡᔍᬊĥ⫮ᮥᙹพ⦹ᩍ
⩲᮹⦹ࠥಾ⧉ᮝಽᕽ, ݡȽ༉ᨱթḡᔍᬊᖅၰᔍᨦᨱݡ⦽ᔍᱥ á☁ಽᬱᱢᯙᨱթḡᱩ᧞ᮥᮁࠥ⦹Ł᪉ᝅaᜅ႑⇽qᗭෝ☖⦽
ʑ⬥ᄡ⪵⩲᧞ᨱ܆࠺ᱢᮝಽݡ⦹ᩍǎaᨱթḡᙹɪℕĥ᮹ᱢᱶ
⪵ෝࠥ༉⦹ŁᨱթḡᙹɪǍ᳑᮹ ᯕᬊ⬉ᮉ⨆ᔢ᮹ᝅ⩥ᮥ᭥⦽
ᱽࠥᯕ݅.(ᨱթḡšญŖ݉2011). ᨱթḡᔍᬊĥ⫮᮹᯲ᖒᮡᨱ թḡᔍᬊĥ⫮᮹ݡᔢᯕࡹ۵ᔍᨦ᮹႑Ğၰ༊ᱢ॒ᔍᨦ}ᨱ
ݡ⦹ᩍʑᚁ⦹Ł, ᖅᬊḡᄥಽᨱթḡᙹᇡྙᮥᖅᱶ⦹Łb
ᇡྙᨱᕽၽᔾ⦹۵ᩕၰᱥಆᨱթḡᙹᨱݡ⧕ᩩ⊂ᯱഭෝᱽ
⦽݅. əญŁᨱթḡᙹᩩ⊂ᯱഭᨱෙḲ݉ᨱթḡŖɪĥ⫮
॒}ᄥÕ⇶ྜྷ᮹ӽႊႊŝᱥಆŖɪႊᦩᮥđᱶ⦹ᩍᨱթḡŖɪ ĥ⫮ᮥ᯲ᖒ⦹Ł, ݡᔢᔍᨦḡǍԕੱ۵}ᄥÕ⇶ྜྷԕᨱᨱթḡ ᯕᬊ⬉ᮉᮥ⨆ᔢ⦹۵ᖅእॅᨱݡ⦽᯦ࠥĥ⫮ᮥ᯲ᖒ⦹۵ߑ, ᯕ᪡
ᇩᨕᝁᰍᔾᨱթḡᔾᔑၰŖɪᖅĥ⫮॒ᨱݡ⦹ᩍʑᚁ⧕
⦽݅. ᝁᰍᔾᨱթḡᖅእෝ⡍⧉⦽ᨱթḡᯕᬊ⬉ᮉ⨆ᔢᖅእ॒᮹
᯦ࠥᨱෙᨱթḡᱩq⬉ŝၰᩢ⨆ᮥᇥᕾ⦹ᩍᨱթḡᯕᬊ⨆
ᔢĥ⫮ᮥᙹพ⦹Ł, ᨱթḡᯕᬊ⬉ᮉ⨆ᔢᖅእ॒᮹ᖅ⊹šญĥ
⫮ ၰ ᔍᨦ݉ĥᄥ ᔍ⬥šญĥ⫮ ॒ᮥ ʑᰍ⧕ ⦽݅.
3. ᨱթḡᙹᔍᱥᇥᕾ
3.1 ंজ۩ঃ
ᅙᩑǍ᮹ᇥᕾݡᔢᮡԉ᧲ᵝḥᱲᮮᨱ᭥⊹⦽Ŗ࠺ᵝ┾݉ḡ ಽ, 16࠺1,479ᖙݡಽǍᖒࡹᨕᯩᮝ໑, 2010֥3ᬵᨱᵡŖ⦹ᩍ
5ᬵᨱ↽Ⅹ᯦ᵝ⦹ᩡ݅. Ŗɪᵝ┾ᮡᱥᬊ໕ᱢʑᵡᮝಽ39.72m²,
46.90m², 51.93m² ᯕ໑, ᖙݡ᮹⠪Ɂᱥᬊ໕ᱢᮡ47.03 m² ᯕ݅.
ᝁᗮ⦹Łᝍࠥʫᮡ᳑ᔍෝ᭥⦹ᩍᱥᙹ᳑ᔍݡᝁᨱ⢽ᅙ᳑ᔍෝ
⧪⦹ᩍᱥℕḲ݉᮹✚ᖒᮥ⇵ᱶ⦹ʑಽ⦽݅. ᇥᕾ⬉ŝෝɚݡ⪵
⦹ʑ᭥⧕Table 2᪡zᯕᩑǍݡᔢ݉ḡ᮹ᇢ⊂Ş᮹⠙ᅖࠥ⩶ᯙ
⠪Ɂᱥᬊ໕ᱢᨱɝᱲ⦹۵ݡ⢽⠪⩶ᮝಽอǍᖒࡽ࠺ᮥ⢽ᅙᮝಽ
ᖁᱶ⦹ᩡ݅. ӽႊႊᮡ }ᄥӽႊᮝಽᕽ ӽႊ໕ᱢ ॒ᮥ ⪶ᯙ⧁
ᙹ ᯩ۵ ݉᭥ᖙݡ⠪໕ࠥ۵ Fig. 2᪡ z݅.
3.2 ୨ܑBINୢॺళࠤ࣑
ࠥ}ၽᔍᨦ᮹ᨱթḡᔍᬊĥ⫮ᙹพ☖ᔢᱢᮝಽᔍᬊࡹ۵
ᙹᱶ᪉ࠥBIN ᱥᔑญჶᮝಽᅙᩑǍ᮹ݡᔢᨱݡ⦽aᜅ(ӽႊ,ɪ
┶) ᇡ⦹ప᮹ᙹෝᩩ⊂⦹ᩡ݅. ᙹᱶ᪉ࠥBIN ᱥᔑญჶᯕ
ᩍ్Ğᬑ᮹ʑ᪉ࠥᨱݡ⦽Õྜྷ᮹ᙽeᨱթḡෝĥᔑ⦹ᩍ1֥࠺
ᦩၽᔾ⦽eᙹෝŒ⦹ᩍⅾ֥eᇡ⦹ෝǍ⦹۵ႊჶᯙ⢽ᵡBIN ჶᮥၽᱥ┉ႊჶᮝಽ1֥᮹8,760eᮥ3e݉᭥ಽӹ٥ᨕ
ʑᔢℎᨱᕽ⊂ᱶ⦽↽ɝ10֥eʑᔢߑᯕ░ᨱᕽʑ᪉ࠥ⠪Ɂs
ᮥǍ⦹Ł, ᖅĥᝅԕ᪉ࠥ₉᪡ᝅᱽᝅԕ᪉ࠥ₉ෝݡእ⍽
⠪⩶᪉ࠥᯕ⦹᮹ʑ᳑Õᨱᕽᱥᔑ⥥ಽəఉᨱ ᮹⧕ᩕᙹෝ
ᔑᱶ⦹۵ႊჶᯕ໑, ᮁ⦽ᗭ⊹ᱥᔑญႊჶᯕŁࠥ⦽݅. ᙹᱶ ᪉ࠥBIN ᱥᔑญჶᨱᕽᔍᬊࡹ۵ӽႊᇡ⦹పᔑ⇽ŝɪ┶ᇡ⦹
ప ᔑ⇽ᮡ ݅ᮭŝ z݅.
G ӽႊᇡ⦹ప ᔑ⇽
Ə
¥Þ ƁſƊîƗß á
Ɔ á Îā
ÕÔÓ×ãć Þƒ
Ƈà ƒ
×ß
ä
Ə¥ : ᝅᱽ ӽႊᇡ⦹Þ ƁſƊîƗß ƏƆ : ӽႊ ݉᭥ᩕᇡ⦹ÞƉƁſƊîƋÏ_Ɔß
Ɔ : ӽႊ໕ᱢÞƋÏß
Ɔ : ӽႊeÞƆß
ƒƇ ìƒ× : ᖅĥ ᝅԕ᪉ࠥ, ᖅĥ ʑ᪉ࠥ(ⳃ)
ᝅᱽ᪉ࠥ
ƒƀ : ⠪⩶ᱱ᪉ࠥ(ⳃ),
ƒ
ƀá ƒ
Ƈà Þª
ƅîƏ
ƆßÞƒ
Ƈà ƒ
×ß
G ɪ┶ᇡ⦹ప ᔑ⇽
Ə
Þ ƁſƊîƗß á
ƕ á Îā
ÕÔÓ×ÞƏ
ƕZ
ƕZ
ƕZ ķ Z ĸß
Ə : ᝅᱽɪ┶ᇡ⦹Þ ƁſƊîƗß Əƕ : ݉᭥ɪ┶ᇡ⦹ÞƉƁſƊ_ƋÏîƆß
ƕ : ɪ┶໕ᱢÞƋÏß
ƕ : ɪ┶eÞƆß
ķì ĸ : ᬵᄥɪ┶ᇡ⦹ᮉ, eݡᄥᔍᬊᮉ
Table 3. Input variables calculated heating load
Application target area
Design outdoor temperature
Design space temperature
Equipoint temperature
Unit Heating
Load Apartment Houses,
Metropolitan Area -11.3 °C 20 °C 16°C 51.4 kcal/
Table 4. Input variables produce hot water load
Month Jan Feb Mar Apr May Jun Jul Aug Sep Sep Oct Dec
Hot water
load factor 1 0.99 0.87 0.76 0.63 0.51 0.35 0.31 0.40 0.54 0.63 0.98
Time 0~3 3~6 6~9 9~12 12~15 15~18 18~21 21~24
Use rate 0.1 0.4 1 0.6 0.5 0.5 1 0.1
Fig. 3. Shape information of the building Fig. 4. 3D Modelling of the building Fig. 5. Zoning of the building Table 2᪡zᯕ݉᭥ᖙݡ᮹ӽႊ໕ᱢᮡ41.57m² ᯕᨩᮝအಽ
ᩑǍݡᔢᱥℕ75ᖙݡ᮹ⅾӽႊᩑ໕ᱢᮡ3117.75 m² ᯕ݅. ᨱթḡ ᔍᬊĥ⫮ᙹพၰ⩲᮹ᱩ₉॒ᨱš⦽Ƚᱶᨱ᮹⦽əၷ᮹᯦ಆᄡᙹ
ॅᮥTable 3ŝzᯕᖅᱶ⦹ᩡ݅. ᯝᔍపၰԕᇡၽᩕపᮥŁಅ⦽
⠪⩶ᱱ᪉ࠥ۵24eԕ࠺ᯝ⦹í16°Cಽᖅᱶ⦹ᩡŁ, 3e݉᭥
᮹ᝅᱽʑ᪉ࠥ۵ʑᔢℎš⊂ᯱഭෝ⪽ᬊ⦹ᩡ݅. ᩑǍݡᔢʑe
࠺ᦩ(2010֥12ᬵ~ 2011֥2ᬵ, 3}ᬵ)᮹ӽႊᇡ⦹పᮥᙹᱶ᪉ࠥ
BINᱥᔑญჶᮥ⪽ᬊ⦹ᩍᔑ⇽⧕ᅙđŝ230Gcal ᩡ݅. ੱ⦽, ᨱթḡᔍᬊĥ⫮ᙹพၰ⩲᮹ᱩ₉॒ᨱš⦽Ƚᱶᨱ᮹⧕Ŗ࠺ᵝ┾
ŝeݡᄥᔍᬊශᮡTable 4᪡zᯕᱢᬊ⦽đŝ࠺ᱩʑɪ┶ᇡ⦹
పᮡ 53Gcal ᩡ݅.
3.3 Սࢄনۇਏ࢘ߑଲ
Õྜྷᖒ܆ဍ౩ᯕᖹ᮹༊ᱢᮡᙹ⦺ᱢ༉ߙᮥᔍᬊ⦹ᩍ, Õྜྷ᮹
ྜྷญᱢᔢ┽ෝᩩ⊂⦹۵äᯕ݅. ဍ౩ᯕᖹᔍᬊᯱ۵Õྜྷᖒ܆
ဍ౩ᯕᖹᮥ☖⧕ᝅᱽ(reality)ᨱɝᱲ⦽Õྜྷ᮹ᖒ܆ᮥᩩ⊂⦹۵
äᯕa܆⧁ᐱᦥܩ, Õྜྷᖒ܆ᨱᩢ⨆ᮥᵝ۵ԕ ᇡᄡᙹॅᨱ
ݡ⧕ᔢᖙ⯩ᇥᕾ⧁ᙹᯩ݅.(ᯕᵡᬑ2010) ᯕ్⦽Õྜྷᖒ܆ဍ౩ ᯕᖹᮡᱡᨱթḡÕྜྷᮥĥ⫮⦹ʑ᭥⧕Ⅹʑᖅĥ݉ĥᇡ░Õྜྷᖒ
܆ၰᨱթḡෝḥ݉⦹Ł⠪a⦹۵॒Ųჵ᭥⦹í⪽ᬊࡹŁᯩ݅.
ᅙᩑǍݡᔢᮥ࠺ᱩʑᨱɪ᷾⦹۵ӽႊᨱթḡ᭥ᵝಽဍ౩ᯕ ᖹ⦹ᩡ۵ߑÕྜྷᖒ܆ဍ౩ᯕᖹႊჶၰᱩ₉۵Revit Architec- ture 2011ᮥ⪽ᬊ⦹ᩍᩑǍݡᔢ᮹Fig. 3ŝzᮡ⩶ᔢᱶᅕෝFig.
4᪡zᯕ3D ಽ༉ߙย⦹ŁgbXML(Green Building XML)ᮥ
⇵⇽⦽अ, Design Builder(v.3.0)ෝᔍᬊ⦹ᩍᅕ݅e⠙⦹íဍ ౩ᯕᖹ༉ߙยᮥ⦹ᩡ݅. ༉ߙยᖙݡᄥಽᨱթḡᗭእaᯕᨕ
Table 5. Simulation input variables
Input value Value
Clothing (clo) 1.00
Setting internal temperature () 20
Lighting (lux) 100
Occupants Number of occupants
Lighting
energy consumption (W/m²) 5
Radiant fraction 0.42
Visible fraction 0.18
Equipments energy
energy consumption (w/m²) 2.16
Radiant faction 0.20
Air change rate (Air change/h) 0.70
Heating method
Type Radiant/ Convective
Location floor
Heating Radiant Fraction 0.2
City gas COP 0.8
DHW
Type Instantaneous
CoP(%) 84.3
Delivery Temperature() 60
Table 6. The simulation results
Classification Gas(heating) consumption Average consumption Gas(Heating) 9777.75 Nm³(102.00 Gcal) 1.45 Nm³
Fig. 6. Energy consumption according to the unit household personel
Table 7. Winter average energy consumption per capita Classification Electricity Gas Total
Average
consumption 230.46 kwh 77.31 Nm³ -
Ton of Oil
Equivalent 0.24 TOE 0.01 TOE 0.25 TOE
* TOE = fuel quantity (˜, kg, Nm3, kWh) × toe ÷ 1,000toe/kgoe toe referred by energy fundamental law Clause I, Article V, Electricity (kwh): 0.215, gas (Nm³) :1.055
Fig. 7. Electric (left) and gas (right) usage according to average age
ḡ۵ᱱᮥqᦩ⦹ᩍFig. 5 ౝᖙݡᄥಽǍ⫮(zoning)⦹ᩡŁ,
᪥ᖒࡽဍ౩ᯕᖹ༉ߙᮡEnegy Plus(v.7.1)ෝᔍᬊ⦹ᩍ↽᳦ᱢ ᮝಽÕྜྷᨱթḡෝᇥᕾ⦹ᩡ݅. ᩑǍݡᔢ᮹ʑ᳑Õᮡ⦽ǎᕽᬙ ḡᩎ᮹ʑᔢߑᯕ░ෝᔍᬊ⦹ᩡŁ, əၷ᮹ဍ౩ᯕᖹᮥᝅ⧪⦹ʑ
᭥⦽ ᯦ಆᄡᙹ۵ Table 5᪡ zᯕ ᖅᱶ⦹ᩍ 12ᬵ 1ᯝᇡ░ 2ᬵ
28ᯝʭḡ࠺ᱩʑ3}ᬵ࠺ᦩ᮹ӽႊᨱթḡᔍᬊప᮹ဍ౩ᯕᖹ
ᇥᕾᮥ ᙹ⧪⦽ đŝ۵ Table 6ŝ z݅.
4. ᝅߑᯕ░⪽ᬊᨱթḡᙹ➉▕ᇥᕾ
4.1 প۩٪૬൪ഋंজ
ᅙᩑǍݡᔢᨱ۵⩥ᰍ220ᯕ᯦ᵝ⦹ᩍᔾ⪽⦹Łᯩ݅. ᖙݡ ᬱᙹ2ŝ3ᮝಽǍᖒࡽᖙݡa54ᖙݡ(72%)ಽaᰆฯᦹŁ, 4ŝ5ᮝಽǍᖒࡽᖙݡa19ᖙݡ(25%), 1ᯙᖙݡa2ᖙݡ(3%) ᙽᯕᨩ݅. Fig. 6ᨱᕽᙹᯩॐᯕᖙݡᬱᙹᨱෙᨱթḡᗭእప
ᮡᖙݡᬱᙹa᷾a⧁ᙹಾᨱթḡᗭእࠥእಡ⦹ᩍ᷾a⦹۵äᮥ
⪶ᯙ⧁ᙹᯩ݅. ᯕভ᮹ᨱթḡᗭእపᮡᱥʑᨱթḡ᪡aᜅᨱթḡ
ෝᕾᮁ⪹ᔑ★(TOE)ᮝಽ⪹ᔑ⦽sᮝಽ⢽⩥⦹ᩡŁ, bʑǍᇥࡽ
ᨱթḡᬱᄥ࠺ᱩʑ1ᯙݚ⠪ɁᨱթḡᗭእపᮡTable 7ŝzᦹ݅.
⦽⠙, ᖙݡᄥǍᖒᬱ᮹⠪Ɂᩑಚᨱෙ࠺ᱩʑᗭእ⦽ᱥʑⅾ ᔍᬊప(kwh)ŝaᜅⅾᔍᬊప(Nm³)ᮥᔕ⠕ᅕ໕Fig. 7ŝzᯕ✚
ᄥ⦽➉▕ᨧᯕ᭥ᦥ௹ಽᇥ⡍⦹Łᯩᮭᮥᙹᯩ݅. bᔑᱱࠥ᮹
Fig. 8. Electric (left) and gas (right) usage according to outside temperature
Fig. 9. Electric (left) and gas (right) usage according to humidity
Fig. 10. Electric (left) and gas (right) usage according to wind velocity
⇵ᖙᖁᮥ ᅕࠥ ࢱ ᄡᙹe ᔢššĥ۵ ᨧ۵ äᮝಽ ᅕᯕ໑,
݅อaᜅᔍᬊపᮡ⠪Ɂᩑಚᯕᱢᮝ໕݅ᗭฯᦥḡ۵Ğ⨆ᮥᅝ
ᙹ ᯩ݅.
4.2 ׆บ٪૬൪ഋंজ
ᅙᩑǍݡᔢᯙɝʑᔢš⊂ḡᱱᯙԉ᧲ᵝḥÕ᮹ʑᔢℎᔢᖙš
⊂ᯱഭAWS(Automatic Weather System) ᵲʑ᪉, ࠥ, ⣮ᗮߑ ᯕ░ෝ⇵⇽⦹ᩍbb᮹ʑᔢ᳑Õŝeݚᖙݡ⠪Ɂᱥʑᔍᬊప (kwh) ၰaᜅᔍᬊప(Nm³)᮹ᔢššĥෝᇥᕾ⦹ᩡ݅. ᯕভᵲℊ
ࡹ۵ʑᔢ᳑ÕᯕᯩᮥĞᬑ⧕ݚ᳑Õᨱᗭእࡹ۵ᱥʑၰaᜅᔍᬊ ప᮹⠪Ɂపᮥᔑ⇽⦹ᩍᇥᕾ⦹ᩡ݅. ʑᔢ᳑Õᄡ⪵పݡእᱥʑ
ၰaᜅᔍᬊప᮹ᄡ⪵పᨱݡ⧕Fig. 8, 9, 10᮹ᔑᱱࠥෝ☖⧕
}ťᱢᯙᔢššĥෝ❭ᦦ⧕ᅕ໕, ᯝ݉ᱥʑᔍᬊపᮡʑ᪉, ࠥ,
⣮ᗮ᮹ᩢ⨆ᮥÑ᮹ၼḡᦫᮭᮥᙹᯩ݅. ᯕ۵᳑ʑǍၰ
ʑ┡ᔾ⪽⦹໕ᕽ⦥⦽ᱥʑʑʑ᮹ᔍᬊᱶࠥ᪡ʑᔢ᳑Õŝ᮹š ĥ۵ᖁ⩶ᱢᯙᔢššĥa᧞⧉ᮥ⪶ᯙ⧁ᙹᯩ݅. ə్ӹaᜅᔍᬊ పᮡࠥၰ⣮ᗮ✚⯩ʑ᪉ŝv⦽ᔢššĥෝᅕᯕ۵ߑᯕ۵
ᯕ్⦽ʑ᪉᳑Õॅᯕ≉ᔍᇡ⦹పᮡᯝᱶ⦹݅Łaᱶ⧁ ভɪ┶
ၰ ӽႊᇡ⦹పŝ᮹ ၡᱲ⦽ šĥa ᯩᮭᮥ ⇵ᱶ⧕ ᅝ ᙹ ᯩ݅.
ʑᔢ᳑Õŝᨱթḡᔍᬊపᷪ, ࢱᄡᙹ᮹ᩑšࠥ(measures of association)ෝ⊂ᱶ⧁ᙹᯩ۵ݡ⢽ᱢࠥ۵Ŗᇥᔑ(covariance) ŝᔢšĥᙹ(coefficient of correlation)aᯩ۵ߑTable 8ŝzᯕ
ʑ᪉ࠥ᪡eݚᖙݡ⠪Ɂaᜅᔍᬊప(Nm³)᮹ᔢšĥᙹ۵- 0.762 ᮝಽ ࢱ ᄡᙹeᨱ۵ ᇡ(-)᮹ v⦽ ᖁ⩶šĥᨱ ᯩᮭᮥ
ᙹ ᯩ݅.
݅ᮭᮝಽᦿᕽᇥᕾ⦽v⦽ᖁ⩶šĥaᯩ۵ᇡ᪉ࠥ(t,ࠦพᄡ
Table 8. Energy consumption correlation coefficient according to weather condition
Classification Electricity consumption Gas consumption
Covariance Correlation coefficient Covariance Correlation coefficient
Temperature - 0.025 - 0.083 - 0.332 - 0.762
Humidity 0.151 0.171 0.295 0.281
Wind velocity - 0.013 - 0.334 - 0.019 - 0.354
Fig. 11. Simple regression analysis of the line fit
Fig. 12. Monthly total energy consumption
Table 9. Day of the week energy use ratio (%)
Classification Mon Tue Wed Tur Fri Sat Sun Electricity
consumption 96 87 96 97 95 97 100
Gas consumption 93 80 86 88 83 95 100
Fig. 13. Hourly average electric usage
Fig. 14. Hourly average gas usage ᙹ)ᄡ⪵ᨱෙŖ࠺ᵝ┾ᗭእᨱթḡᵲaᜅᔍᬊప(y,᳦ᗮᄡᙹ)᮹
ᩩ⊂పᮥᦥᅕʑ᭥⧕ࢱᄡᙹe݉ᙽ⫭ȡᇥᕾᮥᙹ⧪⦹ᩡ݅.
ভ᮹᳦ᗮᄡᙹᯙaᜅᔍᬊపᮡ⧕ݚʑ᪉ࠥ(°C)ᨱᔍᬊࡹ۵ᖙݡ
ݚ⠪Ɂaᜅᔍᬊప(Nm³)ᯕŁ, ᩑǍʑeᵲʑ᪉ࠥaᵲℊࢁĞ ᬑ, ⠪Ɂ☖ĥపᮥᇥᕾᔍᬊ⦹ᩡ݅. ᇥᕾđŝ⇵ᱶࡽ1₉ᖁ⩶⫭
ȡᮡ y(Nm³) = - 0.003 t(°C) + 0.087 ᯕŁ đᱶĥᙹ(R²)᮹
sᮡ 0.581 ᮝಽ ӹ┡ԍ݅.
4.3 ਏԩ۩٪૬൪ഋंজ
ᅙםྙ᮹ᩑǍʑeᯙ࠺ᱩʑ࠺ᦩᬵᄥᗭእࡹ۵ᨱթḡᔍᬊప
ᮥእƱᇥᕾ⦹ʑ᭥⧕ᱥʑⅾᔍᬊప(kwh)ŝaᜅⅾᔍᬊప(Nm³)
ᮥ⧊ᔑ⦹ᩍᕾᮁ⪹ᔑ★ᮝಽӹ┡ԕᨕᅕ໕Fig. 12᪡zᯕ2010֥
12ᬵᨱ۵7.00 TOE ෝ, 2011֥1ᬵᨱ۵12.83 TOE ෝ, 2012֥
2ᬵᨱ۵9.56 TOE ෝᗭእ⦹ᩡ݅. 2010֥12ᬵ᮹⠪Ɂʑ᪉ᯕ
-2.97°C, 2011֥1ᬵ᮹⠪Ɂʑ᪉ᯕ-10.15°C, 2011֥2ᬵ᮹⠪Ɂ ʑ᪉ᯕ-0.85°C ᯕᨩᮭᮥŁಅ⦹໕ʑ᪉ࠥaaᰆԏᮡ1ᬵᨱ
ᨱթḡᗭእపᯕ aᰆ ฯᮭᮥ ᙹ ᯩ݅.
ᯝᄥᨱթḡᗭእ۵᯦ᵝᯱ᮹ᰍᝅeᯕᔢݡᱢᮝಽʕ☁
ᯝŝᯝᯝᯕ݅ෙᯝᨱእ⧕ฯᮡäᮥᙹᯩ݅. ᨱթḡᗭእప ᯕ݅ෙᯝᅕ݅۵ᨱթḡᗭእపᯕฯᮭᮥᙹᯩ݅. ᯕ۵᯦ᵝᯱ
a ᰍᝅe ࠺ᦩ ᳑ʑǍ᮹ šᱢ ᔍᬊእᮉ(93%) (ʡ⬉ᯙ
2012) ၰʑ┡ᔾ⪽aᱥʑʑ᮹ᔍᬊእᮉ, ӽႊᯕӹɪ┶ၰ≉ᔍᇡ
⦹పᯕᔢݡᱢᮝಽ᷾aࡹᨕᨱթḡᗭእపᯕฯᦥḱᮥ⇵ᱶ⧁ᙹ
ᯩ݅. ᨱթḡᔍᬊపᯕ aᰆ ฯᮡ ᯝᯝ᮹ ⠪Ɂ ᱥʑᔍᬊప ၰ
aᜅᔍᬊపᮥʑᵡᮝಽʑ┡ᯝ᮹ᔍᬊపእᮉᮥᔑ⇽⦹ᩍᅕ໕
݅ᮭTable 9᪡zᦹŁ, ᵝᵲݡእᵝัᨱ۵9.88% อⓝᨱթḡᔍ ᬊపᯕ ᷾aࢉᮥ ᙹ ᯩᨩ݅.
ษḡสᮝಽeݡᄥᖙݡ⠪Ɂᱥʑᔍᬊపၰ⠪Ɂaᜅᔍᬊప
ᄡ⪵⇵ᯕෝᔕ⠕ᅕ໕Fig. 13, Fig. 14᪡z݅. ⦹ᵲ↽ݡᱥʑ
Fig. 15. Floor total energy consumption
Fig. 16. Household arrangement
Fig. 17. Location-specific total energy consumption
Table 10. Electricity usage t-test results
Classification No. 1 household No. 5 household
Average 4.738 4.396
Dispersion 1.090 1.085
Number of observation 2160 2160
Pooled Dispersion 1.088 hypothesis average
difference 0
the degree of freedom 4318 t statistics value 10.770 P(T<=t) one-tailed test 5.190E-27
t Dissmiss value
one-tailed test 1.645 P(T<=t) two tailed test 1.038E-26
t Dissmiss value
two tailed test 1.960 ၰaᜅᔍᬊపᯕᗭእࡹ۵eݡ۵࠺ᯝ⦹ᩡᮝ໑, ↽ᱡᱥʑၰ
aᜅᔍᬊపᯕᗭእࡹ۵eݡ۵ᔢᯕ⦹ᩡ݅. ᖙݡ⠪Ɂᱥʑၰ
aᜅᔍᬊపᯕaᰆฯᦹeݡ۵᪅⬥10ಽᯕভbbᖙݡᄥ
⠪Ɂᔍᬊపᮡ0.418 kwh, 0.180Nm³ ᯙၹ໕, ᖙݡ⠪Ɂᱥʑᔍᬊ పᯕaᰆᱢᨩeݡ᪡ᔍᬊపᮡ᪅ᱥ5, 0.238 kwhᯕᨩŁ, ᖙݡ⠪Ɂaᜅᔍᬊపᯕaᰆᱢᨩeݡ᪡ᔍᬊపᮡ᪅⬥3, 0.054Nm³ᯕᨩ݅. ᯝၹᱢᮝಽᨱթḡᗭእ۵ᰍᝅᯱaฯᮡeݡ ᯙ ᪅ᱥ 6ᇡ░ ᷾a⦹ᩍ 8ᨱ ↽Łᱱᨱ ݍ⧩݅a ᵥᨕॅŁ, ᪅⬥ 6 ᯕ⬥ᨱ ݅ ɪ᷾⦹۵ äᮝಽ ӹ┡ԍ݅.
4.4 վԩ٪૬൪ഋंজ
ᅙםྙ᮹ᩑǍݡᔢᮡ15⊖Ŗ࠺ᵝ┾ᮝಽ1⊖ᖙݡॅᮥ↽⦹⊖
ᖙݡ, 15⊖ᖙݡॅᮥ↽ᔢ⊖ᖙݡ, ʑ┡ᖙݡॅᮥʑ┡⊖ᖙݡಽ
Ǎᇥ⦹ᩍ ࠺ᱩʑ ᖙݡᄥ ⠪Ɂ ᨱթḡᔍᬊపᮥ Fig. 15᪡ zᯕ
እƱ⧕ᅕ໕, ↽⦹⊖ᮡ0.512TOE, ↽ᔢ⊖ᮡ0.465TOE, ʑ┡⊖ᮡ
0.377TOEಽ↽⦹⊖᮹ᨱթḡᔍᬊపᯕʑ┡⊖ݡእ35%, ↽ᔢ⊖
ݡእ 10%a ฯᦹ݅.
ੱ⦽ᄞᨱḢᱲ໕⦽⊂ᖙݡ᪡ʑ┡ᵲᦺᖙݡ᮹ᨱթḡᗭእప
ᮥእƱ⧕ᅕᦹ݅. ᩑǍݡᔢᮡFig. 16ŝzᯕ⦽⊖ᨱ5ᖙݡಽ
Ǎᖒࡹᨕᯩ۵ߑᄞᨱḢᱲ໕⦽1⪙᪡5⪙, əญŁᵲᦺᖙݡᯙ
2⪙, 3⪙, 4⪙ಽǍᇥ⧕ᕽᇥᕾ⦽đŝ࠺ᱩʑᗭእࡹ۵ⅾᨱթḡᔍ ᬊపᮡ⊂ᖙݡ0.405TOE, ᵲᦺᖙݡ0.383TOE ᮝಽ⊂ᖙݡ۵
ᵲᦺᖙݡʑᵡ᧞6%᮹ᨱթḡᔍᬊపᯕฯᮡäᮝಽӹ┡ԍ݅.
⦽⠙Fig. 16᮹⊂ᖙݡᯙ࠺⊂1⪙ᖙݡ᪡ᕽ⊂5⪙ᖙݡ᮹ᖙݡ႑
⊹✚ᖒᨱෙᨱթḡᗭእపᮥእƱ⧕ᅕᦹ݅. ࠺ᱩʑ1⪙ᖙݡ᪡
5⪙ᖙݡ᮹ᨱթḡᔍᬊపᨱݡ⦽₉ᯕa☖ĥᱢᮝಽᮁ᮹⦽ḡෝ
᳑ᔍ⦹ʑ᭥⦹ᩍaᖅáᱶ(t-áᱶ)ᮥᙹ⧪⦹ʑಽ⦽݅. 1⪙ᖙݡ᮹
ᱥʑᔍᬊపၰaᜅᔍᬊపᮡ5⪙ᖙݡ᮹ᱥʑᔍᬊపၰaᜅᔍᬊప ŝࠦพᱢᯕအಽbb᮹ᨱթḡᔍᬊపᮥ༉ࢱ áᱶᨱᔍᬊ⦹ᩍ
₉ᯕᇥᕾᮥ⦹ᩡ݅. “1⪙ᖙݡ᪡5⪙ᖙݡ᮹ᱥʑၰaᜅᔍᬊపᮡ
࠺ᯝ⦹݅”۵ʑᅙaᖅᮥᖅᱶ⦹Łaᖅáᱶ(t-áᱶ)ᮥᙹ⧪⦽đŝ
Table 10 əญŁTable 11ŝzᯕᱥʑၰaᜅᔍᬊప༉ࢱt-☖ĥప
sᯕ₥┾ᩎၷᨱ᳕ᰍ⦹အಽʑᅙaᖅᮥ ʑb⦹Łݡพaᖅᮥ
ၼᦥॅᯕ۵đು, ᷪ“1⪙ᖙݡ᪡5⪙ᖙݡ᮹࠺ᱩʑᱥʑၰaᜅᔍ ᬊపᨱ۵₉ᯕa᳕ᰍ⦽݅”۵đುᮥԕตᙹᯩᮝ໑, “1⪙ᖙݡ᮹
eݚᱥʑၰaᜅᔍᬊపᯕ5⪙ᖙݡᅕ݅ฯ݅”۵äᮡ☖ĥᱢᮝ ಽ᮹ၙaᯩ݅. ᯕ۵1⪙ᖙݡ᪡5⪙ᖙݡ᮹࠺ᕽ⊂ᖙݡ႑⊹Ǎ᳑
ᨱᯝ᳑e, ᯝᔍప॒᮹ʑ┡⪹Ğᯙᯕ᳑ʑǍ॒᮹
Table 11. Gas usage t-test results
Classification No. 1 household No. 5 household
Average 1.861 1.518
Dispersion 2.393 1.871
Number of observation 2160 2160
Pooled Dispersion 2.132 hypothesis average
difference 0
the degree of freedom 4318 t statistics value 7.731 P(T<=t) one-tailed test 6.549E-15
t Dissmiss value
one-tailed test 1.645 P(T<=t) two tailed test 1.309E-14
t Dissmiss value
two tailed test 1.960
Table 12. Electricity usage t-test results
Classification Jan 1. 2011 Jan 1. 2012
Average 24.056 23.978
Dispersion 19.906 19.859
Number of observation 216 216
Pooled Dispersion 0.867 Hypothesis average difference 0
The degree of freedom 215 t statistics value 0.497 P(T<=t) one-tailed test 0.309 t Dissmiss value one-tailed test 1.651 P(T<=t) two tailed test 0.619
t Dissmiss value
two tailed test 1.971
Table 13. Gas usage t-test results
Classification Jan 1. 2011 Jan 1. 2012
Average 11.234 10.021
Dispersion 22.904 21.226
Number of observation 216 216
Pooled Dispersion 0.626 Hypothesis average difference 0
The degree of freedom 215 t statistics value 4.386 P(T<=t) one-tailed test 9.004E-06 t Dissmiss value one-tailed test 1.651
P(T<=t) two tailed test 1.80E-05 t Dissmiss value
two tailed test 1.971 ᔍᬊᮝಽᯙ⦽ᱥʑᨱթḡ, ӽႊ॒ᮝಽᯙ⦽aᜅᨱթḡ᮹ᗭእᱶ
ࠥᨱ ᩢ⨆ᮥ ၙ⊽݅۵ äᮥ ⇵ᱶ⧁ ᙹ ᯩᨩ݅.
4.5 ܑ࣫ఢড౿ߦ٪ীण߆ंজ
ᅙᩑǍݡᔢᮡ2011֥8ᬵ࠺ᱩʑᖙݡԕđಽႊḡၰᨱթḡᯕ ᬊ⬉ᮉ⨆ᔢᮥ᭥⦹ᩍÕྜྷ⬥໕ᖙݡ⩥šᦿᅖࠥ⊂ᨱ⧊ᖒᙹḡᰍ
⪙ෝᖅ⊹⦹ᩡ݅. ŝᩑᅖࠥ⊂⪙ᖅ⊹ಽᯙ⦽ᝅḩᱢᯙᖙݡ
ԕᨱթḡᱩq⬉ŝaษӹࡹ۵ḡᔕ⠕ᅕʑಽ⦽݅. Ł⬉ᮉᨱթ ḡʑᯱᰍᅕɪⅪḥᨱš⦽Ƚᱶᨱ᮹⦹ᩍ⊂ᱶ⦽đŝ݉ᩕᖒ܆ᮡ
ᔍᬊపᯕaᰆฯᮡ1ᬵ᮹ᱥʑᔍᬊపၰaᜅᔍᬊపᮥ☁ݡಽ
ᨱթḡᗭእపᇥᕾᮥ⦹ᩡ۵ߑ2011֥1ᬵ᮹⠪Ɂʑ᪉ŝ2012֥
1ᬵ᮹⠪Ɂʑ᪉ᮡ₉ᯕaᯩʑভྙᨱࢱḲ݉ᨱթḡᔍᬊప᮹
݉ᙽእƱ ᇥᕾᮡ᮹ၙaᨧᨕ, ᱶ⪶⦽đŝෝࠥ⇽⦹ʑ᭥⦹ᩍ
ᯝ⠪Ɂʑ᪉ᯕ እ⦽ ✚ᱶᯝᮥ ⢽ᅙᮝಽ ᖁᱶ⦹ᩍ ᇥᕾ⦹ᩡ݅.
bb⢽ᅙᮝಽᖁᱶࡽʑe࠺ᦩ᮹2011֥1ᬵ᮹ᱥʑᔍᬊపၰ
aᜅᔍᬊపŝ2012֥1ᬵ᮹ᱥʑᔍᬊపၰaᜅᔍᬊపᮥᖙݡᄥ
⦽ᝮᮝಽš⊂ࡽ⢽ᅙ᮹₉ᯕෝᯕᬊ⦹ᩍษ₍aḡಽ“2011֥
1ᬵ᮹⠪Ɂᨱթḡᔍᬊపŝ2012֥1ᬵ᮹⠪Ɂᨱթḡᔍᬊపᮡz
݅”۵ʑᅙaᖅᮥᖅᱶ⦹Łt-áᱶ(ᝮℕእƱ)ᮥᙹ⧪⦽đŝᱥʑᔍ ᬊపᮡᱩq⬉ŝŁ⧁อ⦽ⓑᄡ⪵aᨧᨩŁaᜅᔍᬊపᮡ⠪Ɂ
☖ĥపݡእ᧞10.8%᮹ᱩq⬉ŝෝaᲙ᪵݅. aᜅᔍᬊప᮹qᗭ ۵ʑᵡᯕᔢ᮹݉ᩕᖒ܆ŝʑၡᖒ܆ᮥw۵ᅖࠥ⊂⪙aʑ᪡
᮹ḢᱲᱢᯙᱲⅪᮥสᦥӽႊᨱթḡ⊂໕ᨱᕽ᮹ᱩq⬉ŝaəݡ ಽ ၹᩢࡽ đŝŁ ⇵ᱶᯕ ࡽ݅.
5. đು
ᅙᩑǍᨱᕽ۵݉ᯝÕྜྷᮡᦥܩḡอᨱթḡ݅ᗭእǑᯙŖ࠺ᵝ
┾ᯕ۵ᇡᇥᱢᯙᙹ݉᮹ᝅeᮝಽᔾᖒ ᱡᰆࡹ۵ᨱթḡᔍ ᬊపߑᯕ░ෝᙹḲ⦹Łᨱթḡᗭእᨱᩢ⨆ᮥᵥᙹᯩ۵ᄡᙹෝ
ᖅᱶ⦹ᩍ ☖ĥ ᇥᕾ⧉ᮝಽ៉ ݅ᮭŝ zᮡ đŝෝ ᨩ݅.
(1) ᖙݡᬱᙹa᷾a⧁ᙹಾᨱթḡᗭእࠥᯝᱶపᯕᔢ᷾a⦹ᩡᮝ ໑, ᱥʑᗭእపŝ۵ݍญaᜅᗭእపᮡ᯦ᵝᯱ᮹ᖙݡǍᖒᬱ᮹
⠪Ɂᩑಚᯕ ᱢᮝ໕ ݅ᗭ ฯᦥḡ۵ Ğ⨆ᮥ ᅕᩡ݅.
(2) ࠺ᱩʑᱥʑᗭእపᮡʑᔢ᳑Õ᮹ᩢ⨆ᮥÑ᮹ၼḡᦫ۵äᮝಽ
ӹ┡ԍᮝӹ, aᜅᔍᬊపᮡࠥၰ⣮ᗮ✚⯩ʑ᪉᮹ᩢ⨆ᮥ
ฯᯕ ၼŁ ᯩᮭᮥ ⪶ᯙ⧁ ᙹ ᯩᨩ݅.
(3) ࠺ᱩʑᵲ⠪Ɂʑ᪉ᯕaᰆԏᮡ1ᬵ᮹ᨱթḡᗭእపᯕaᰆ
ฯᦹᮝ໑, ᯦ᵝᯱ᮹ᰍᝅ⪶ශᯕᔢݡᱢᮝಽ׳ᮡᵝัᨱᵝᵲ
ݡእ9.88% ᱶࠥᨱթḡᔍᬊపᯕ᷾aࡹᨩŁ, ษ₍aḡಽ⦹
ᵲᰍᝅᯱaฯᮡ᪅ᱥ6᪡᪅⬥6ᯕ⬥ಽᨱթḡᗭእa
ɪ᷾⦹ᩡŁ, ᪅⬥ 10ᨱ ᨱթḡᗭእa aᰆ ฯᦹ݅.
(4) ↽ᔢ⊖, ↽⦹⊖, ʑ┡⊖ᮝಽǍᇥ⦹ᩍᇥᕾ⦽đŝ↽⦹⊖᮹
ᨱթḡᔍᬊపᯕ ʑ┡⊖ ݡእ 35%, ↽ᔢ⊖ ݡእ 10%a
ฯᦹ݅. əญŁʑᨱᔢݡᱢᮝಽฯᯕי⇽ࡽ⊂ᖙݡaᵲᦺ ᖙݡ ᅕ݅ 6% ᱶࠥ ᨱթḡᔍᬊపᯕ ฯᦹ݅.
(5) ࠺ᱩʑᇡᅖࠥ⊂⪙ᖅ⊹ಽᯙ⦽⪙ᖅ⊹⬥᮹aᜅᔍᬊప
ᮡ ᖅ⊹ ᱥ᮹ aᜅᔍᬊప ݡእ ᧞ 10.8% ᱩqࡹᨩ݅. ၹ໕
ᱥʑᔍᬊప᮹ ᱩq⬉ŝ۵ ᨧᨩ݅.
ᯕ్⦽ᩑǍđŝ۵↽ᱢ᮹ᨱթḡᱩ᧞ᖅĥෝ᭥⦽ʑⅩᯱഭಽ
៉᮹⪽ᬊŝǢɚᱢᮝಽu-City᪡zᮡၙ௹ࠥÕᖅᨱᯩᨕ⬉ᮉᱢ ᯕŁᦩᱶࡽᨱթḡᙹšญෝ᭥⦽ᯱഭಽ⪽ᬊࡹʑෝʑݡ⦽݅.
qᔍ᮹ɡ
ᅙᩑǍ۵ǎ☁⧕᧲ᇡ᮹u-City ᕾ ၶᔍŝᱶḡᬱᔍᨦ, ǎ☁
⧕᧲ᇡ℉݉ࠥ}ၽᔍᨦ(11℉݉ࠥG04), 2011֥ࠥḡĞᱽ
ᇡḡĞᱽR&Dᱥఖʑ⫮݉(OSP)No.2011T100100022)᮹ᩑ Ǎእḡᬱᨱ ᮹⧕ ᙹ⧪ࡹᨩܩ݅.
