본문 바로가기 주메뉴 바로가기
22대 대한민국 국회 국회정보길라잡이 국회의원검색
회원가입 로그인
HOME

정보검색

소장정보 검색

국회도서관 홈으로 정보검색 소장정보 검색

결과 내 검색

동의어 포함

고급검색

인명/단체명

저자정보 상세정보
인명/단체명을 입력하세요.

키워드

  • 대표어

  • 외국어

-

목차보기

표제지

목차

Nomenclature 6

Greek Symbols 7

1. 서론 12

1.1. 연구목적 및 필요성 12

1.2. 국내·외 연구동향 14

1.3. 연구내용 및 연구방법 16

2. 기상데이터 예측방법 19

2.1. 시간대별 기상데이터 무차원변수 도출 19

2.2. 퍼지 알고리즘을 이용한 최대/최소값 추론 25

2.3. 기상데이터 예측 결과 및 분석 31

3. 부하 평가 모델 39

3.1. EnergyPlus를 사용한 부하 해석 40

3.1.1. 기준건물 설정 40

3.1.2. 직교배열표를 이용한 실험계획 47

3.1.3. 부하해석 결과 51

3.2. 비례/반비례 관계를 이용한 부하 모델링 55

3.2.1. 변수의 단순화 55

3.2.2. 부하해석 모델링 62

3.2.3. 부하해석 결과 및 분석 68

3.3. 열평형법을 적용한 부하 모델링 70

3.3.1. 벽체에 의한 부하 70

3.3.2. 창호의 전도에 의한 부하 73

3.3.3. 창호의 일사투과에 의한 부하 75

3.3.4. 벽체와 창호에 의한 구조체부하 76

3.3.5. 부하해석 결과 및 분석 77

4. 부하 연동 환기장치 제어 85

4.1. 부하 연동 제어방법 86

4.2. 결과 및 분석 92

5. 결론 98

REFERENCES 101

ABSTRACT 105

표목차

Table 2-1. Weather data measuring point. 19

Table 2-2. Coefficients for temperature correlation. 22

Table 2-3. Coefficients for relative humidity correlation. 23

Table 2-4. Coefficients for insolation correlation. 23

Table 2-5. Linguistic variable and its mark. 26

Table 2-6. Fuzzy rule for the maximum relative humidity in summer. 30

Table 2-7. Fuzzy rule for the minimum relative humidity in summer. 30

Table 2-8. Fuzzy rule for the maximum insolation in summer. 30

Table 2-9. Fuzzy rule for the maximum relative humidity in winter. 30

Table 2-10. Fuzzy rule for the minimum relative humidity in winter. 30

Table 2-11. Fuzzy rule for the maximum insolation in sinter. 30

Table 3-1. Two conditions of each parameter. 43

Table 3-2. Setting properties for wall structure. 44

Table 3-3. Thickness of wall structure. 44

Table 3-4. Properties of window. 45

Table 3-5. Table of orthogonal arrays. 49

Table 3-6. Geographical features of Daejeon. 49

Table 3-7. Analysis of variance table for cooling period. 52

Table 3-8. Analysis of variance table for heating period. 53

Table 3-9. Reflectance(ρg) for each state of earth's surface.(이미지참조) 58

Table 3-10. Setting equation. 63

Table 3-11. Obtained constants by regression in cooling period. 64

Table 3-12. Obtained constants by regression in heating period. 66

Table 4-1. Indoor setting conditions. 89

Table 4-2. Linguistic values and their marks. 90

Table 4-3. Fuzzy rules for fan flow. 91

Table 4-4. The amount of CO₂ emission per person. 92

Table 4-5. Average CO₂ concentration. 95

Table 4-6. Operation number. 95

Table 4-7. Comparison of cooling loads. 95

Table 4-8. Adjusted fuzzy rules for fan flow. 97

그림목차

Fig. 2-1. Variation of hourly average outdoor condition of each month. 20

Fig. 2-2. Variation of normalized hourly average outdoor condition for five years. 24

Fig. 2-3. Fuzzy set of maximum temperature. 27

Fig. 2-4. Fuzzy set of minimum temperature. 27

Fig. 2-5. Fuzzy set of temperature difference. 27

Fig. 2-6. Fuzzy set of maximum relative humidity. 27

Fig. 2-7. Fuzzy set of minimum relative humidity. 27

Fig. 2-8. Fuzzy set of maximum insolation. 28

Fig. 2-9. Process finding nearly optimized fuzzy rule. 29

Fig. 2-10. Comparison of predicted temperature with measured data. 33

Fig. 2-11. Comparison of predicted relative humidity with measured data. 34

Fig. 2-12. Comparison of predicted insolation with measured data. 35

Fig. 2-13. Comparison of predicted temperature with measured data. 36

Fig. 2-14. Comparison of predicted relative humidity with measured data. 37

Fig. 2-15. Comparison of predicted insolation with measured data. 38

Fig. 3-1. Setting outdoor condition for simulation. 41

Fig. 3-2. Comparison of inside insulation with outside insulation. 46

Fig. 3-3. Zone shape of examples. 50

Fig. 3-4. F-distribution for cooling period. 54

Fig. 3-5. F-distribution for heating period. 54

Fig. 3-6. Comparison of calculated data with citing data in summer. 59

Fig. 3-7. Comparison of calculated data with citing data in winter. 60

Fig. 3-8. Shape of zone. 61

Fig. 3-9. Comparison of modelling load with simulation load. 69

Fig. 3-10. Heat inflow and outflow at wall. 72

Fig. 3-11. Comparison of modeling load with simulation load for wall. 79

Fig. 3-12. Comparison of modeling load with simulation load for conduction by window. 81

Fig. 3-13. Comparison of modeling load with simulation load for conduction by window. 83

Fig. 4-1. Allowable CO₂ concentration for ON/OFF control. 90

Fig. 4-2. Fuzzy membership function. 91

Fig. 4-3. Comparison of indoor CO₂ concentration. 94

Fig. 4-4. Variation of indoor CO₂ concentration in case of adjusted fuzzy rule. 97

Fig. 4-5. Comparison of fan flow rate. 97

초록보기

As the modern buildings are gradually air-tightened for enrgy saving and indoor air quality is getting worse, the mechanical ventilation is demanded. The existing ventilation controls doesn't consider outdoor condition which influence on outdoor load. The purpose of this study is to develop ventilation control method considering outdoor cooling/heating loads as well as indoor air quality. For this, outdoor condition and building load are necessary. Outdoor condition can be predicted knowing the variation pattern and max/min figure of the next day. and by analysing outdoor condition data of 5 years, the variation pattern is obtained. By using fuzzy algorithm, the method which can inference relative humidity and insolation through forecasted temperature is studied. Predicted outdoor condition show good agreement with the measured data. Two method evaluating load having the merit of static simulation and imitating the result of dynamic simulation is studied. One is composed of proportion/inverse proportion factors for building load, and another uses heat balance method appling to wall structure. As a result of comparing with dynamic simulation results the proposed methods nearly can copy results of that. For controlling the mechanical ventilation, two control methods, control of allowable CO2 concentrations and control of fan flow rates, are investigated and the results are compared. Allowable concentrations are adjusted using a ON/OFF control, and fan flow rates are controlled using a fuzzy algorithm. Comparing with the existing control method, a little increase of CO2 concentration is found, but more than 10% of energy is saved. If the appropriate correction of fuzzy rule is made depending on the site, more positive results could be obtained.

추천서가 (다양한 추천 자료를 만나보세요)

권호기사보기

권호기사 목록 테이블로 기사명, 저자명, 페이지, 원문, 기사목차 순으로 되어있습니다.
기사명 저자명 페이지 원문 기사목차

권호

기사명 원문보기 기사목차
닫기